{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6jGRS-9syu-L"
   },
   "source": [
    "# Feature Neutralization\n",
    "\n",
    "One thing that makes predicting the stock market so hard is the \"non-stationary\" relationship between features and returns. Features can have strong predictive power some eras but not others - or may completely reverse over time.\n",
    "\n",
    "This uncertainty is what we call \"feature risk\". In order to create models that have consistent performance, it is helpful to reduce this feature risk via \"feature neutralization\". In this notebook, we will:\n",
    "\n",
    "1. Learn how to quantify feature risk\n",
    "2. Measure our model's feature exposure\n",
    "3. Apply feature neutralization to our predictions\n",
    "4. Measure the performance of our neutralized predictions\n",
    "5. Pickle and upload our feature-neutral model"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!python --version"
   ],
   "metadata": {
    "id": "ws4qrSssFC9T",
    "outputId": "3860d6e5-38ec-4638-82b2-bce4c7365966",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2025-10-30T20:48:41.241956Z",
     "start_time": "2025-10-30T20:48:40.890069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.11.11\r\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iHzZde7Tyu-N",
    "outputId": "f9cb52f5-88f3-4776-a1be-cef458e718f5",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:48:46.319068Z",
     "start_time": "2025-10-30T20:48:41.350277Z"
    }
   },
   "source": [
    "# Install dependencies\n",
    "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
    "\n",
    "# Inline plots\n",
    "%matplotlib inline"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m25.2\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.3\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZGyNf56dyu-O"
   },
   "source": [
    "## 1. Feature Risk\n",
    "\n",
    "In order to quantify feature risk, we evaluate the performance of each feature on their own."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K7uvuNlAyu-P"
   },
   "source": [
    "### Feature Groups\n",
    "In the last notebook, you learned about the basic feature sets that Numerai offers. There are also 8 feature groups: `intelligence`, `wisdom`, `charisma`, `dexterity`, `strength`, `constitution`, `agility`, `serenity`. Each group contains a different type of feature. For example all technical signals would be in one group, while all analyst predictions and ratings would be in another group.\n",
    "\n",
    "Let us take a look at feature groups in the small, medium, and all feature sets:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 385
    },
    "id": "JTN8-MUmyu-P",
    "outputId": "b8d0557f-ae8f-48e8-e707-806ac4683ad4",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:48:46.969257Z",
     "start_time": "2025-10-30T20:48:46.335971Z"
    }
   },
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "from numerapi import NumerAPI\n",
    "\n",
    "# initialize our API client\n",
    "napi = NumerAPI()\n",
    "\n",
    "# Set data version to one of the latest datasets\n",
    "DATA_VERSION = \"v5.1\"\n",
    "\n",
    "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n",
    "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
    "feature_sets = feature_metadata[\"feature_sets\"]\n",
    "\n",
    "sizes = [\"small\", \"medium\", \"all\"]\n",
    "groups = [\n",
    "  \"intelligence\",\n",
    "  \"wisdom\",\n",
    "  \"charisma\",\n",
    "  \"dexterity\",\n",
    "  \"strength\",\n",
    "  \"constitution\",\n",
    "  \"agility\",\n",
    "  \"serenity\",\n",
    "  \"all\"\n",
    "]\n",
    "\n",
    "# compile the intersections of feature sets and feature groups\n",
    "subgroups = {}\n",
    "for size in sizes:\n",
    "    subgroups[size] = {}\n",
    "    for group in groups:\n",
    "        subgroups[size][group] = (\n",
    "            set(feature_sets[size])\n",
    "            .intersection(set(feature_sets[group]))\n",
    "        )\n",
    "\n",
    "# convert to data frame and display the feature count of each intersection\n",
    "pd.DataFrame(subgroups).applymap(len).sort_values(by=\"all\", ascending=False)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 13:48:46,901 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 13:48:46,907 INFO numerapi.utils: download complete\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/2052836788.py:39: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  pd.DataFrame(subgroups).applymap(len).sort_values(by=\"all\", ascending=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "              small  medium   all\n",
       "all              42     740  2562\n",
       "constitution      2     134   335\n",
       "charisma          3     116   290\n",
       "agility           2      58   145\n",
       "wisdom            3      56   140\n",
       "strength          1      54   135\n",
       "serenity          3      34    95\n",
       "dexterity         4      21    51\n",
       "intelligence      2      14    35"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>small</th>\n",
       "      <th>medium</th>\n",
       "      <th>all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>42</td>\n",
       "      <td>740</td>\n",
       "      <td>2562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constitution</th>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charisma</th>\n",
       "      <td>3</td>\n",
       "      <td>116</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agility</th>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wisdom</th>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strength</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serenity</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dexterity</th>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intelligence</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 29
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMy4I8cBO3aG"
   },
   "source": [
    "For this tutorial we are going to analyze the `serenity` features from the `small` feature sets.\n",
    "\n",
    "We read the entire `small` training feature set to train on it later:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "meowEBs-PwtB",
    "outputId": "b82484be-38ce-4524-fdf9-60bbd038f09f",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:48:50.602505Z",
     "start_time": "2025-10-30T20:48:47.003662Z"
    }
   },
   "source": [
    "# define the small features and small serenity features\n",
    "# use \"all\" for better performance. Requires more RAM.\n",
    "feature_size = \"small\"\n",
    "# feature_size = \"all\"\n",
    "small_features = feature_sets[feature_size]\n",
    "med_serenity_feats = list(subgroups[feature_size][\"serenity\"])\n",
    "\n",
    "# Download the training data and feature metadata\n",
    "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
    "\n",
    "# Load the just the small feature set,\n",
    "# this is a great feature of the parquet file format\n",
    "train = pd.read_parquet(\n",
    "    f\"{DATA_VERSION}/train.parquet\",\n",
    "    columns=[\"era\", \"target\"] + small_features\n",
    ")\n",
    "\n",
    "# Downsample to every 4th era to reduce memory usage and\n",
    "# speedup model training (suggested for Colab free tier).\n",
    "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 13:48:47,674 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 13:48:47,683 INFO numerapi.utils: download complete\n"
     ]
    }
   ],
   "execution_count": 30
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qUdZvVSPyu-Q"
   },
   "source": [
    "### Evaluating feature performance\n",
    "\n",
    "When thinking about feature risk, the first thing to check might be the correlation of each feature with the target over the training dataset. This will tell us what kind of relationship each feature has with the target:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 561
    },
    "id": "SE9QmW6ryu-Q",
    "outputId": "53d5a554-bfbe-4719-c359-8f0ffa1fd970",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:00.306109Z",
     "start_time": "2025-10-30T20:48:50.620834Z"
    }
   },
   "source": [
    "# install numerai-tools\n",
    "!pip install -q --no-deps numerai-tools\n",
    "\n",
    "# import numerai_corr, you can read the source code here:\n",
    "# https://github.com/numerai/numerai-tools/blob/master/numerai_tools/scoring.py\n",
    "from numerai_tools.scoring import numerai_corr\n",
    "import numpy as np\n",
    "\n",
    "# Compute the per-era correlation of each serenity feature to the target\n",
    "per_era_corr = train.groupby(\"era\").apply(\n",
    "    lambda d: numerai_corr(d[med_serenity_feats], d[\"target\"])\n",
    ")\n",
    "\n",
    "# Flip sign for negative mean correlation since we only care about magnitude\n",
    "per_era_corr *= np.sign(per_era_corr.mean())\n",
    "\n",
    "# Plot the per-era correlations\n",
    "per_era_corr.cumsum().plot(\n",
    "    title=\"Cumulative Absolute Value CORR of Features and the Target\",\n",
    "    figsize=(15, 5),\n",
    "    legend=False,\n",
    "    xlabel=\"Era\"\n",
    "  )"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m25.2\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.3\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/2623001418.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  per_era_corr = train.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Cumulative Absolute Value CORR of Features and the Target'}, xlabel='Era'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 31
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_tMfPJ4Gyu-Q"
   },
   "source": [
    "Let's compute some summary performance metrics as we did in the previous notebook for our model predictions.\n",
    "\n",
    "Notice above that some features can perform extremely differently at different times over the validation period. To measure how much the performance of the feature changes over this period, we introduce a new metric:\n",
    "\n",
    "- `delta` is the absolute difference in `mean` correlation between the first and second half of the analysis period."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "88KzLZGCyu-R",
    "outputId": "9dfa901f-4da3-4d03-a70a-751f08f05537",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:00.580767Z",
     "start_time": "2025-10-30T20:49:00.340498Z"
    }
   },
   "source": [
    "def metrics(corr):\n",
    "    corr_mean = corr.mean()\n",
    "    corr_std = corr.std(ddof=0)\n",
    "    corr_sharpe = corr_mean / corr_std\n",
    "    max_drawdown = -(corr.cumsum().expanding(min_periods=1).max() - corr.cumsum()).max()\n",
    "\n",
    "    eras = train.era.unique()\n",
    "    halfway_era = len(eras)//2\n",
    "    corr_mean_first_half = corr.loc[eras[:halfway_era]].mean()\n",
    "    corr_mean_second_half = corr.loc[eras[halfway_era:]].mean()\n",
    "    delta = abs(corr_mean_first_half - corr_mean_second_half)\n",
    "\n",
    "    return {\n",
    "      \"mean\": corr_mean,\n",
    "      \"std\": corr_std,\n",
    "      \"sharpe\": corr_sharpe,\n",
    "      \"max_drawdown\": max_drawdown,\n",
    "      \"delta\": delta\n",
    "    }\n",
    "\n",
    "# compute performance metrics for each feature\n",
    "feature_metrics = [\n",
    "    metrics(per_era_corr[feature_name])\n",
    "    for feature_name in med_serenity_feats\n",
    "]\n",
    "\n",
    "# convert to numeric DataFrame and sort\n",
    "feature_metrics = (\n",
    "    pd.DataFrame(feature_metrics, index=med_serenity_feats)\n",
    "    .apply(pd.to_numeric)\n",
    "    .sort_values(\"mean\", ascending=False)\n",
    ")\n",
    "\n",
    "feature_metrics"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                                mean      std   sharpe  \\\n",
       "feature_glandered_unimproved_peafowl        0.000899 0.004457 0.201743   \n",
       "feature_elusive_vapoury_accomplice          0.000619 0.006414 0.096574   \n",
       "feature_unsystematized_subcardinal_malaysia 0.000334 0.007446 0.044894   \n",
       "\n",
       "                                             max_drawdown    delta  \n",
       "feature_glandered_unimproved_peafowl            -0.024903 0.000329  \n",
       "feature_elusive_vapoury_accomplice              -0.033173 0.000904  \n",
       "feature_unsystematized_subcardinal_malaysia     -0.054091 0.000301  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>feature_glandered_unimproved_peafowl</th>\n",
       "      <td>0.000899</td>\n",
       "      <td>0.004457</td>\n",
       "      <td>0.201743</td>\n",
       "      <td>-0.024903</td>\n",
       "      <td>0.000329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_elusive_vapoury_accomplice</th>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.006414</td>\n",
       "      <td>0.096574</td>\n",
       "      <td>-0.033173</td>\n",
       "      <td>0.000904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_unsystematized_subcardinal_malaysia</th>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.007446</td>\n",
       "      <td>0.044894</td>\n",
       "      <td>-0.054091</td>\n",
       "      <td>0.000301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "odM2_6-Hyu-R"
   },
   "source": [
    "Looking at the summary visualizations below, the most obvious observation is that `mean` and `sharpe` seem strongly correlated. This should not be suprising given that `sharpe` is just `mean` divided by `std`.\n",
    "\n",
    "A more interesting obvservation is that `mean` does not seem to be strongly correlated with `std`, `max_drawdown`, or `delta`. This tells us very clearly that just because a feature has high `mean` does not mean that it is consistent or low risk.\n",
    "\n",
    "In the next section we more closely examine `std`, `max_drawdown`, and `delta` to better understand feature risk."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 656
    },
    "id": "mqqdKda_yu-R",
    "outputId": "7473f9b4-57b6-4988-94c8-7ed843248358",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:01.375368Z",
     "start_time": "2025-10-30T20:49:00.644103Z"
    }
   },
   "source": [
    "# plot the performance metrics of the features as bar charts sorted by mean\n",
    "feature_metrics.sort_values(\"mean\", ascending=False).plot.bar(\n",
    "    title=\"Performance Metrics of Features Sorted by Mean\",\n",
    "    subplots=True,\n",
    "    figsize=(15, 6),\n",
    "    layout=(2, 3),\n",
    "    sharex=False,\n",
    "    xticks=[],\n",
    "    snap=False\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'mean'}>,\n",
       "        <Axes: title={'center': 'std'}>,\n",
       "        <Axes: title={'center': 'sharpe'}>],\n",
       "       [<Axes: title={'center': 'max_drawdown'}>,\n",
       "        <Axes: title={'center': 'delta'}>, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 6 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 33
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QgrZJXRnyu-S"
   },
   "source": [
    "### Comparing feature risk\n",
    "\n",
    "Below is a performance comparison of the highest and lowest `std` features. Which one looks more risky to you and why?\n",
    "\n",
    "One might argue that the orange line looks more risky given its more sudden and violent reversals. Extrapolating forward, we may expect this volatility to continue out of sample."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "XVQe5lnAyu-S",
    "outputId": "8d3f8818-ed7e-4f7a-bdc0-96540bbb71b2",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:02.078901Z",
     "start_time": "2025-10-30T20:49:01.395445Z"
    }
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot the per era correlation of the feature with the highest vs lowest std\n",
    "per_era_corr[[feature_metrics[\"std\"].idxmin(), feature_metrics[\"std\"].idxmax()]].plot(\n",
    "    figsize=(15, 5), title=\"Per-era Correlation of Features to the Target\", xlabel=\"Era\"\n",
    ")\n",
    "plt.legend([\"lowest std\", \"highest std\"])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x392c04690>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 34
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uv6H5CnWyu-S"
   },
   "source": [
    "Below is a comparison of the highest and lowest `delta` features. Which one looks more risky to you and why?\n",
    "\n",
    "One might argue that the orange line looks more risky given the complete reversal in performance between the first and second half, despite both ending up in a similar spot. Extraoploating forward, we may expect this feature to stop working completely out-of-sample."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "5hgFAmOOyu-S",
    "outputId": "5d9b15a5-c027-4cdd-e349-93e39965b8f1",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:02.472476Z",
     "start_time": "2025-10-30T20:49:02.112510Z"
    }
   },
   "source": [
    "# plot the cumulative per era correlation of the feature with the highest vs lowest delta\n",
    "per_era_corr[[feature_metrics[\"delta\"].idxmin(), feature_metrics[\"delta\"].idxmax()]].cumsum().plot(\n",
    "    figsize=(15, 5), title=\"Cumulative Correlation of Features to the Target\", xlabel=\"Era\"\n",
    ")\n",
    "plt.legend([\"lowest delta\", \"highest delta\"])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16a036150>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 35
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D6XD2knayu-S"
   },
   "source": [
    "Below is a comparison of the highest and lowest `max_drawdown` features. Which one looks more risky to you and why?\n",
    "\n",
    "One might argue that the orange line is more risky given the huge drawdown in the middle, despite both ending up in a similar spot. Extrapolating forward, we may expect it to have another big drawdown out of sample."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "xlFsPKNzyu-T",
    "outputId": "418e6341-930b-49c7-bb99-8303712d819f",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:02.928950Z",
     "start_time": "2025-10-30T20:49:02.509696Z"
    }
   },
   "source": [
    "# plot the cumulative per era correlation of the feature with the highest vs lowest max_drawdown\n",
    "per_era_corr[[feature_metrics[\"max_drawdown\"].idxmax(), feature_metrics[\"max_drawdown\"].idxmin()]].cumsum().plot(\n",
    "    figsize=(15, 5), title=\"Cumulative Correlation of Features to the Target\", xlabel=\"Era\"\n",
    ")\n",
    "plt.legend([\"lowest max_drawdown\", \"highest max_drawdown\"])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x152a25a90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 36
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AMBFbIvNyu-T"
   },
   "source": [
    "The metrics analyzed above are only a few of many different ways you can quantify feature risk.\n",
    "\n",
    "What are some other ways you can think of?\n",
    "\n",
    "Think about this while we train a model on the entire small feature set."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 180
    },
    "id": "0nm5VBXy4UBK",
    "outputId": "fe8deacb-6e34-42ed-ba13-bd079fe06c01",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:03:17.006528Z",
     "start_time": "2025-10-30T20:49:03.046799Z"
    }
   },
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "model = lgb.LGBMRegressor(\n",
    "    n_estimators=2000,\n",
    "    learning_rate=0.01,\n",
    "    max_depth=5,\n",
    "    num_leaves=2**4-1,\n",
    "    colsample_bytree=0.1\n",
    ")\n",
    "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
    "# model = lgb.LGBMRegressor(\n",
    "#     n_estimators=30_000,\n",
    "#     learning_rate=0.001,\n",
    "#     max_depth=10,\n",
    "#     num_leaves=2**10,\n",
    "#     colsample_bytree=0.1\n",
    "#     min_data_in_leaf=10000,\n",
    "# )\n",
    "model.fit(\n",
    "    train[small_features],\n",
    "    train[\"target\"]\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.128392 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 210\n",
      "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
      "[LightGBM] [Info] Start training from score 0.500008\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
       "              n_estimators=2000, num_leaves=15)"
      ],
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
       "              n_estimators=2000, num_leaves=15)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('boosting_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">boosting_type&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('num_leaves',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">num_leaves&nbsp;</td>\n",
       "            <td class=\"value\">15</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">0.01</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">2000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
       "            <td class=\"value\">200000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('objective',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">objective&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_split_gain',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
       "            <td class=\"value\">20</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_freq',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_lambda',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('importance_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">importance_type&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;split&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
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       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
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       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
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       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
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       "        });\n",
       "    return false;\n",
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       "\n",
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       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 37
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7DXXnuUPyu-T"
   },
   "source": [
    "## 2. Feature Exposure\n",
    "\n",
    "`Feature exposure` is a measure of a model's exposure to the risk of individual features, given by the Pearson correlation between a model's predictions and each feature. Let's load up and predict on the validation data for our small feature set."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1fZmZVFuyu-T",
    "outputId": "c7a4fee8-e158-4184-91bb-7f77a07a9ccf",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:12.889650Z",
     "start_time": "2025-10-30T21:25:58.493910Z"
    }
   },
   "source": [
    "# Download validation data\n",
    "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
    "\n",
    "# Load the validation data, filtering for data_type == \"validation\"\n",
    "validation = pd.read_parquet(\n",
    "    f\"{DATA_VERSION}/validation.parquet\",\n",
    "    columns=[\"era\", \"data_type\", \"target\"] + small_features\n",
    ")\n",
    "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
    "del validation[\"data_type\"]\n",
    "\n",
    "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n",
    "# Comment out the line below to use all the data\n",
    "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
    "\n",
    "# Embargo overlapping eras from training data\n",
    "last_train_era = int(train[\"era\"].unique()[-1])\n",
    "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
    "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n",
    "\n",
    "# Generate predictions against the small feature set of the validation data\n",
    "validation[\"prediction\"] = model.predict(validation[small_features])"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:25:59,088 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:25:59,088 INFO numerapi.utils: download complete\n"
     ]
    }
   ],
   "execution_count": 39
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "48t8e3Huyu-U"
   },
   "source": [
    "### Visualizing feature exposures\n",
    "\n",
    "As seen in the chart below, our model seems to be consistently correlated to a few features. If these features suddenly reverse or stop working, then our model predictions will likely exhibit the same risky characteristics we saw above.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "mExyr3VSyu-U",
    "outputId": "09689cdb-2349-4d75-c015-4e6b6ef1567b",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:13.578624Z",
     "start_time": "2025-10-30T21:26:12.891043Z"
    }
   },
   "source": [
    "# Compute the Peason correlation of the predictions with each of the\n",
    "# serenity features of the small feature set\n",
    "feature_exposures = validation.groupby(\"era\").apply(\n",
    "    lambda d: d[med_serenity_feats].corrwith(d[\"prediction\"])\n",
    ")\n",
    "\n",
    "# Plot the feature exposures as bar charts\n",
    "feature_exposures.plot.bar(\n",
    "    title=\"Feature Exposures\",\n",
    "    figsize=(16, 10),\n",
    "    layout=(7,5),\n",
    "    xticks=[],\n",
    "    subplots=True,\n",
    "    sharex=False,\n",
    "    legend=False,\n",
    "    snap=False\n",
    ")\n",
    "for ax in plt.gcf().axes:\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.title.set_fontsize(10)\n",
    "plt.tight_layout(pad=1.5)\n",
    "plt.gcf().suptitle(\"Feature Exposures\", fontsize=15)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/685944140.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  feature_exposures = validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Feature Exposures')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1000 with 35 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 40
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b7d9TTlIyu-U"
   },
   "source": [
    "### Max feature exposure\n",
    "\n",
    "When reviewing the visualizations above, the scale and consistency of exposure changes feature-to-feature.\n",
    "\n",
    "Can you think of a better way to visualize this?\n",
    "\n",
    "A more useful way to visualize the overall feature exposure of our model might be to look at the maximum feature exposure each era. This is a simple way for us to estimate the maximum exposure the model has to any one feature at any given time.\n",
    "\n",
    "Note that we are only measuring the feature exposures of the subset of features we chose to analyze."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "id": "9_rmsQRSyu-U",
    "outputId": "a03250c5-a4c2-4b8c-bc15-8f55647847bd",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:13.675169Z",
     "start_time": "2025-10-30T21:26:13.579599Z"
    }
   },
   "source": [
    "# Plot the max feature exposure per era\n",
    "max_feature_exposure = feature_exposures.max(axis=1)\n",
    "max_feature_exposure.plot(\n",
    "  title=\"Max Feature Exposure\",\n",
    "  kind=\"bar\",\n",
    "  figsize=(10, 5),\n",
    "  xticks=[],\n",
    "  snap=False\n",
    ")\n",
    "# Mean max feature exposure across eras\n",
    "print(\"Mean of max feature exposure\", max_feature_exposure.mean())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of max feature exposure 0.05043283365774808\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 41
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zQangXyGyu-U"
   },
   "source": [
    "## 3. Feature Neutralization\n",
    "\n",
    "Clearly the model has some consistent exposure to the features on which it was trained.\n",
    "\n",
    "`Feature Neutralization` is a way to reduce these feature exposures.\n",
    "\n",
    "At a high level, neutralizing to a feature means removing the component of your predictions (or \"signal\") that is correlated with that feature, leaving only the residual unique component of the signal.\n",
    "\n",
    "Read these forum posts if you want to learn more about the math behind the feature neutralization:\n",
    "- https://forum.numer.ai/t/model-diagnostics-feature-exposure/899\n",
    "- https://forum.numer.ai/t/an-introduction-to-feature-neutralization-exposure/4955"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fBtfEPPLyu-V"
   },
   "source": [
    "### Applying feature neutralization\n",
    "\n",
    "Let's apply feature neutralization to our predictions at different porportions and see how that impacts max feature exposure."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 618
    },
    "id": "rt2YbOPxyu-V",
    "outputId": "cf6b400e-118f-4324-f17e-1f81034f1f57",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:17.730019Z",
     "start_time": "2025-10-30T21:26:13.677351Z"
    }
   },
   "source": [
    "# import neutralization from numerai-tools\n",
    "from numerai_tools.scoring import neutralize\n",
    "\n",
    "# Neutralize predictions per-era against features at different proportions\n",
    "proportions = [0.25, 0.5, 0.75, 1.0]\n",
    "for proportion in proportions:\n",
    "    neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
    "        lambda d: neutralize(\n",
    "          d[[\"prediction\"]],\n",
    "          d[med_serenity_feats],\n",
    "          proportion=proportion\n",
    "        )\n",
    "    ).reset_index().set_index(\"id\")\n",
    "    validation[f\"neutralized_{proportion*100:.0f}\"] = neutralized[\"prediction\"]\n",
    "\n",
    "# Align the neutralized predictions with the validation data\n",
    "prediction_cols = [\"prediction\"] + [f for f in validation.columns if \"neutralized\" in f]\n",
    "validation[[\"era\", \"target\"] + prediction_cols]"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                   era   target  prediction  neutralized_25  neutralized_50  \\\n",
       "id                                                                            \n",
       "n000c290e4364875  0579 0.500000    0.495167        0.370184        0.245201   \n",
       "n002a15bc5575bbb  0579 0.250000    0.516067        0.390981        0.265896   \n",
       "n00309caaa0f955e  0579 0.750000    0.513778        0.388530        0.263283   \n",
       "n0039cbdcf835708  0579 0.500000    0.507834        0.382948        0.258063   \n",
       "n004143458984f89  0579 0.500000    0.484917        0.360013        0.235109   \n",
       "...                ...      ...         ...             ...             ...   \n",
       "nffc45fef92f9990  1183 0.500000    0.500651        0.375549        0.250448   \n",
       "nffcd993886ef112  1183 0.250000    0.486873        0.361830        0.236787   \n",
       "nffd2f8483ddb3cc  1183 0.250000    0.510465        0.385533        0.260602   \n",
       "nffda3e738a622a7  1183 0.500000    0.494580        0.369530        0.244479   \n",
       "nffdd2cd463ee839  1183 0.500000    0.511365        0.386296        0.261227   \n",
       "\n",
       "                  neutralized_75  neutralized_100  \n",
       "id                                                 \n",
       "n000c290e4364875        0.120218        -0.004765  \n",
       "n002a15bc5575bbb        0.140810         0.015725  \n",
       "n00309caaa0f955e        0.138035         0.012787  \n",
       "n0039cbdcf835708        0.133178         0.008293  \n",
       "n004143458984f89        0.110204        -0.014700  \n",
       "...                          ...              ...  \n",
       "nffc45fef92f9990        0.125346         0.000245  \n",
       "nffcd993886ef112        0.111744        -0.013300  \n",
       "nffd2f8483ddb3cc        0.135671         0.010740  \n",
       "nffda3e738a622a7        0.119429        -0.005621  \n",
       "nffdd2cd463ee839        0.136157         0.011088  \n",
       "\n",
       "[942884 rows x 7 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>era</th>\n",
       "      <th>target</th>\n",
       "      <th>prediction</th>\n",
       "      <th>neutralized_25</th>\n",
       "      <th>neutralized_50</th>\n",
       "      <th>neutralized_75</th>\n",
       "      <th>neutralized_100</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n000c290e4364875</th>\n",
       "      <td>0579</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.495167</td>\n",
       "      <td>0.370184</td>\n",
       "      <td>0.245201</td>\n",
       "      <td>0.120218</td>\n",
       "      <td>-0.004765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n002a15bc5575bbb</th>\n",
       "      <td>0579</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.516067</td>\n",
       "      <td>0.390981</td>\n",
       "      <td>0.265896</td>\n",
       "      <td>0.140810</td>\n",
       "      <td>0.015725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00309caaa0f955e</th>\n",
       "      <td>0579</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.513778</td>\n",
       "      <td>0.388530</td>\n",
       "      <td>0.263283</td>\n",
       "      <td>0.138035</td>\n",
       "      <td>0.012787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0039cbdcf835708</th>\n",
       "      <td>0579</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.507834</td>\n",
       "      <td>0.382948</td>\n",
       "      <td>0.258063</td>\n",
       "      <td>0.133178</td>\n",
       "      <td>0.008293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n004143458984f89</th>\n",
       "      <td>0579</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484917</td>\n",
       "      <td>0.360013</td>\n",
       "      <td>0.235109</td>\n",
       "      <td>0.110204</td>\n",
       "      <td>-0.014700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffc45fef92f9990</th>\n",
       "      <td>1183</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500651</td>\n",
       "      <td>0.375549</td>\n",
       "      <td>0.250448</td>\n",
       "      <td>0.125346</td>\n",
       "      <td>0.000245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffcd993886ef112</th>\n",
       "      <td>1183</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.486873</td>\n",
       "      <td>0.361830</td>\n",
       "      <td>0.236787</td>\n",
       "      <td>0.111744</td>\n",
       "      <td>-0.013300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd2f8483ddb3cc</th>\n",
       "      <td>1183</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.510465</td>\n",
       "      <td>0.385533</td>\n",
       "      <td>0.260602</td>\n",
       "      <td>0.135671</td>\n",
       "      <td>0.010740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffda3e738a622a7</th>\n",
       "      <td>1183</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.494580</td>\n",
       "      <td>0.369530</td>\n",
       "      <td>0.244479</td>\n",
       "      <td>0.119429</td>\n",
       "      <td>-0.005621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffdd2cd463ee839</th>\n",
       "      <td>1183</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.511365</td>\n",
       "      <td>0.386296</td>\n",
       "      <td>0.261227</td>\n",
       "      <td>0.136157</td>\n",
       "      <td>0.011088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942884 rows × 7 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 42
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l0tMOf9Jyu-V"
   },
   "source": [
    "We can see below that, as neutralization proportion reaches 1, feature exposure reaches 0."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 804
    },
    "id": "x-qSdjNQyu-V",
    "outputId": "d246081c-ba90-4bb1-c431-b016fd534728",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:18.981677Z",
     "start_time": "2025-10-30T21:26:17.731319Z"
    }
   },
   "source": [
    "# Compute max feature exposure for each set of predictions\n",
    "max_feature_exposures = pd.concat([\n",
    "    validation.groupby(\"era\").apply(\n",
    "        lambda d: d[med_serenity_feats].corrwith(d[col]).abs().max()\n",
    "    ).rename(col)\n",
    "    for col in prediction_cols\n",
    "], axis=1)\n",
    "\n",
    "# print mean feature exposure of each proportion\n",
    "print('mean feature exposures:')\n",
    "print(round(max_feature_exposures.mean(), 3))\n",
    "\n",
    "# Plot max feature exposures\n",
    "max_feature_exposures.plot.bar(\n",
    "  title=\"Max Feature Exposures\",\n",
    "  figsize=(10, 5),\n",
    "  xticks=[],\n",
    "  snap=False\n",
    ")\n"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  validation.groupby(\"era\").apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  validation.groupby(\"era\").apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  validation.groupby(\"era\").apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean feature exposures:\n",
      "prediction        0.056000\n",
      "neutralized_25    0.042000\n",
      "neutralized_50    0.028000\n",
      "neutralized_75    0.014000\n",
      "neutralized_100   0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Max Feature Exposures'}, xlabel='era'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 43
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GcgsFazTyu-W"
   },
   "source": [
    "### Performance impact of neutralization\n",
    "\n",
    "Looking at the performance below, we see that there is a marginal performance improvement as we increase the porportion of neutralization applied, but the overall shape of the line remains largely the same.\n",
    "\n",
    "You might see below that sometimes the optimal neutralization proportion is not 1.0 over the validation period - seeming to imply that a small amount of feature exposure can sometimes be helpful. After completing this tutorial, continue experimenting with neutralizing at different proportions and analyze the tradeoff between reducing exposure and improving performance."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "zW4f961lyu-W",
    "outputId": "af9d9c85-02c1-46aa-92ed-4e0b272d9148",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:20.991972Z",
     "start_time": "2025-10-30T21:26:18.983102Z"
    }
   },
   "source": [
    "# calculate per-era CORR for each set of predictions\n",
    "correlations = validation.groupby(\"era\").apply(\n",
    "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
    ")\n",
    "\n",
    "# calculate the cumulative corr across eras for each neutralization proportion\n",
    "cumulative_correlations = correlations.cumsum().sort_index()\n",
    "\n",
    "# Show the cumulative correlations\n",
    "pd.DataFrame(cumulative_correlations).plot(\n",
    "    title=\"Cumulative Correlation of Neutralized Predictions\",\n",
    "    figsize=(10, 6),\n",
    "    xticks=[]\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/4274901726.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  correlations = validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Cumulative Correlation of Neutralized Predictions'}, xlabel='era'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 44
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2eAuFO8pyu-W"
   },
   "source": [
    "Let's look at some other aggregate metrics like `mean`, `std`, `sharpe`, and `max_drawdown`.\n",
    "\n",
    "What kind of relationship do you see between neutralization proportion and overall performance?"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "P3YxoLZByu-W",
    "outputId": "c70954c0-e762-4aec-9f4d-27cf29323a5c",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:20.999822Z",
     "start_time": "2025-10-30T21:26:20.993405Z"
    }
   },
   "source": [
    "summary_metrics = {}\n",
    "for col in prediction_cols:\n",
    "    mean = correlations[col].mean()\n",
    "    std = correlations[col].std(ddof=0)\n",
    "    sharpe = mean / std\n",
    "    rolling_max = cumulative_correlations[col].expanding(min_periods=1).max()\n",
    "    max_drawdown = (rolling_max - cumulative_correlations[col]).max()\n",
    "    summary_metrics[col] = {\n",
    "        \"mean\": mean,\n",
    "        \"std\": std,\n",
    "        \"sharpe\": sharpe,\n",
    "        \"max_drawdown\": max_drawdown,\n",
    "    }\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "pd.DataFrame(summary_metrics).T"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    mean      std   sharpe  max_drawdown\n",
       "prediction      0.016520 0.018590 0.888622      0.040911\n",
       "neutralized_25  0.016525 0.018605 0.888235      0.041098\n",
       "neutralized_50  0.016538 0.018594 0.889460      0.041238\n",
       "neutralized_75  0.016524 0.018576 0.889523      0.041385\n",
       "neutralized_100 0.016526 0.018559 0.890468      0.041532"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction</th>\n",
       "      <td>0.016520</td>\n",
       "      <td>0.018590</td>\n",
       "      <td>0.888622</td>\n",
       "      <td>0.040911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_25</th>\n",
       "      <td>0.016525</td>\n",
       "      <td>0.018605</td>\n",
       "      <td>0.888235</td>\n",
       "      <td>0.041098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_50</th>\n",
       "      <td>0.016538</td>\n",
       "      <td>0.018594</td>\n",
       "      <td>0.889460</td>\n",
       "      <td>0.041238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_75</th>\n",
       "      <td>0.016524</td>\n",
       "      <td>0.018576</td>\n",
       "      <td>0.889523</td>\n",
       "      <td>0.041385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_100</th>\n",
       "      <td>0.016526</td>\n",
       "      <td>0.018559</td>\n",
       "      <td>0.890468</td>\n",
       "      <td>0.041532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 45
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t1o7PnCLyu-b"
   },
   "source": [
    "### Neutralizing different groups\n",
    "Given that we trained our model on the entire `small` set of features, it is not surprising that neutralizing just a small subset of 34 features will have a small impact on performance. So let's re-run this experiment but this time try to neutralize the each group within `small` while holding porportion constant at 100%.\n",
    "\n",
    "As we can see in the performance chart below, neutralizing against the different groups gives a much more pronounced impact on performance, which makes sense since these groups are fundamentally different from one another."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 935
    },
    "id": "NKiNDWygyu-b",
    "outputId": "3c04f392-2988-4921-a47f-f487bb88e785",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:34.052509Z",
     "start_time": "2025-10-30T21:26:21.001179Z"
    }
   },
   "source": [
    "# neutralize preds against each group\n",
    "for group in groups:\n",
    "    neutral_feature_subset = list(subgroups[\"small\"][group])\n",
    "    neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
    "        lambda d: neutralize(d[[\"prediction\"]], d[neutral_feature_subset])\n",
    "    ).reset_index().set_index(\"id\")\n",
    "    validation[f\"neutralized_{group}\"] = neutralized[\"prediction\"]\n",
    "\n",
    "group_neutral_cols = [\"prediction\"] + [f\"neutralized_{group}\" for group in groups]\n",
    "group_neutral_corr = validation.groupby(\"era\").apply(\n",
    "    lambda d: numerai_corr(d[group_neutral_cols], d[\"target\"])\n",
    ")\n",
    "group_neutral_cumsum = group_neutral_corr.cumsum()\n",
    "\n",
    "group_neutral_cumsum.plot(\n",
    "  title=\"Cumulative Correlation of Neutralized Predictions\",\n",
    "  figsize=(10, 6),\n",
    "  xticks=[]\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  neutralized = validation.groupby(\"era\", group_keys=True).apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/87142795.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  group_neutral_corr = validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Cumulative Correlation of Neutralized Predictions'}, xlabel='era'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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kIFIuDRnIjYVOOENryPggg9IZe+6EvCatjdPgcT0XPaFuzkSZ9BSS/vC7en+oVCyFnTWErr0xI7Gp8DWaeJGeRB88eFC8d2X27Nni/FQpqaHngN6fKTyvJeMxdepU8a9r6BxBIoxwVpc7G6jiFY0BVVoiA60hVAHsTE+BXa+fKt81dp/QtTYn/Im8cmTo0RNi1/Ei7xeV2XX2xb+FcsjoCfVHH31Uz2tAT6mpktS/6dOGUP+SV4cMwoKCgrO+j+g4VPmP+tg1dLNhGWqnEGzJ/d6SsWwOdD30AOL7779vNBysufcdtYtyZug7nZWVVbedxoiO/19A4Vn0QKKx81EfUxU4gu5Nev3hhx/WbaffpuZMGks5SOQ1Jm9aQ++Z65g093fsXNzfDX/TqL2UO0e0lueNYdor7NFhmDaGjAoK53KWgaZwA5pPg4wZKpVK3gvXOTrIAKe8AvqX/miSEKG5clqb7t27iyR/KtVaVlYmEtCpRLTTeGgK+mNNxj7FiFNSM8XKv//+++LJIsXUNzSs6Mmyc3JAMr6olOrpIKOFigVQGWYytigEp2F/Um4QtZvEFRkqtD89AacEZyrtS59tjfHo3bu3yGUg8ecMMdq1a5cQA3ReZ4L32UDGCok5irunsaAkdYq/p1K6lAhPoteZ29AQCusjLwO1bdGiRUJ0Uj5KYyFj1P/kGaM8DSp3S0+86alwY1A4E7WHPF1UzMJZXpryu1zncPo3UIgjeVjoeqk/KYfEWX6XyldTKeTWhMoBU9/Q03Tq57O5j+i7SHNE0f1OxjeJYfIUNSx3Te8pd4uMXDoWGcd0rzeVu9WSsTwTVA6aypiTZ4i+k9RGV0j0teS+I9G0YsUK4VUlDwL9NtD9QJ9r+D0/F9x///1YunSp8MLSd5LuZcoTo+uk8aBxowdDdD9TCXQKUaR1lG9DHrLmCHz6zaJ7hPqNrpMEMHmpqcw9/V5ReWiCzk1Ciu4Z+gyJmYYem3N1f9P9R7/RdD7KbyJPIo0DPZhw9TQzzAVJgypsDMO0EVQi9cYbb3RERUU5lEqlw8PDwzFs2DDHu+++W6+ELJV5veGGGxxeXl5in7lz5zqKiopOW16ayrY2VtK1IQ3L6xJUhnf8+PGirHNQUJDjkUcecaxevbpZ5aU/++wzUaaWPhsXFydKuDYs3UokJSU5Ro4cKcpb0zZnmd7TlbcmrrrqKrGN2nY6fvnlF8fw4cPFtdJCbVi4cKHjxIkTjtYcD4vF4nj66adF6V2FQuGIiIhwPPzww/X2Iah/qJTz6crP/vTTT422Y//+/Y7Zs2eLsszUl3QcGvO1a9fW7dNYX23dutUxePBg0a+hoaGOBx54wLFy5cpTxk6n0zmuvPJKh7e3t9jmHMfTlTJfs2aN6Ac6LpW0nT59uigp7Mrp7r2mxrQhixcvFmV06Zp9fX3FmOfk5DR6vJaWl26Is4xvw/u/JffR66+/7ggLCxPtpf7Zs2fPKeWliSVLljgSEhJEiWrX/m3s+9fSsTxTeWnnvXa6paX3HbFx40ZR6pi+I1QincpjN/Y9b4zT/RY15HTfHaK6ulp832JiYkQb/P39HUOHDnW89tpropSzEyqJfs0114h7ln476TVd45nKSzv53//+V3c/+vj4iPGi30InBQUFoo30O0Gfd457Y+X4m3t/n65/Grbx559/dkycOFGUraY+6NSpkyg/np+ff8a+ZZiOjoT+19Zii2EYhmEYhmEYpjXhHB2GYRiGYRiGYTocLHQYhmEYhmEYhulwsNBhGIZhGIZhGKbDwUKHYRiGYRiGYZgOBwsdhmEYhmEYhmE6HO1iHh2apIsmCKR5B85mZmmGYRiGYRiGYToGVDS6urpazGdF83+1a6FDIiciIqKtm8EwDMMwDMMwzHlCdna2mCi3XQsd8uQ4L4ZmZWYYhmEYhmEY5sKkqqpKOEGcGqFdCx1nuBqJHBY6DMMwDMMwDMNIzpDSwsUIGIZhGIZhGIbpcLDQYRiGYRiGYRimw8FCh2EYhmEYhmGYDke7yNFpbglqs9nc1s1gmA6HUqlssnQjwzAMwzDM+UiHEDokcDIyMoTYYRimdSGREx0dLQQPwzAMwzBMe0HeESYMys/Ph0wmE2Xm+Mkzw7T+ZL30HevUqRNP2MswDMMwTLuh3Qsdq9UKg8EgZkbVaDRt3RyG6XAEBAQIsUPfNYVC0dbNYRiGYRiGaRbtPvDeZrOJfzmshmHODc7vlvO7xjAMwzAM0x5o90KnuRMGMQzD3y2GYRiGYS4cOozQYRiGYRiGYRiGccJCh2EYhmEYhmGYDgcLnQuAqKgovPXWW/XC/H7//fd/dczWOAbDMAzDMAzDnCvafdU1puVQqWAfH59m7fvUU08JQXPgwIGzPgbDMAzDMAzD/New0GlHk6K2VmW54ODg8+IYDMMwDMMwDHOu6HChazSBqMFsbZOFzt1cRo8ejdtvv10sXl5e8Pf3x+OPP153DAo3e/bZZzFv3jx4enripptuEuu3bNmCESNGQK1WiwlSFy1aBL1eX3fcoqIiTJ8+XWyn2ey//fbbM4ad5eTk4IorroCvry+0Wi369++PnTt34osvvsDTTz+NgwcPis/QQusaO8bhw4cxduxYcV4/Pz/RXp1OV7f9uuuuw6xZs/Daa68hJCRE7LNw4UJYLJYWjzHDMAzDMAzDXHAenRqLDQlPrGyTcx97ZhI0yuZ36ZdffokbbrgBu3btwp49e4Q4oNnnb7zxRrGdRMETTzyBJ598UrxPS0vD5MmT8dxzz+F///sfiouL68TS559/XicoaHLH9evXi8kdSQiR+DkdJEZGjRqFsLAwLF26VHhq9u3bB7vdjssuuwxHjhzBihUrsGbNGrE/ibKGkNCaNGkShgwZgt27d4vzLViwQLTLKYwIahOJHPo3NTVVHD8xMbHuehmGYRiGYRimtehwQqc9QR6ZN998U3hHunXrJrwi9N5p+JOH5N57763bn8TDVVddhbvuuku8j42NxTvvvCOEyocffoisrCwsX75cCKcBAwaIfT777DPEx8eftg3fffedEEwkUMijQ8TExNRtd3d3h1wubzJUjY5hNBrx1VdfCY8Q8d577wnP0ssvv4ygoCCxjnJ6aL1MJkNcXBymTZuGtWvXstBhGIZhGIZhWp0OJ3TUCpnwrLTVuVvC4MGD6010Sh6R119/vW4Gegohc4VCyA4dOlQvHI1C3cj7kpGRgeTkZCFK+vXrV7edBIW3t/dp20BFBvr06VMncs6G48ePo3fv3nUihxg2bJho14kTJ+qETvfu3YXIcULeHRJ3DMMwDMMwzDlEVwQUHgWKjgGVOUBwL6DzKMAztEN3e4cTOiQcWhI+dj7jKhycYWY333yzCEdrCIW8kdBpKZRT819BoXQNx4rEEMMwDMMwDNOKmPVA0l/A4Z+BvH2o0huQi2DkIAQl8IUHkuCHj+Hn6Q7/6B7wDImBxFAESVVe7aIrADyCgKjhQORwILQPIG+dolj/JR1DEbRTKOHflR07dohwNFevhyt9+/bFsWPH6oWWuULeG6vVir1799aFrpFHpaKi4rRt6NWrFz799FOUlZU16tWhSm9OD9PpoNA4ysWhXB2nONu6dSukUqkIyWMYhmEYhmHOMfTwOHMzcPAH4PhS5Ji12IU+yMQMVMGz8c9UUcgQLTl/r/D/e+kFbZEePmkn4INd8JUa4OPnj9CeoxA48rp2M5Qdrupae4Jyau655x4hRr7//nu8++67uPPOO0+7/4MPPoht27aJJH8KOUtJScGSJUvEe4JEBRUrIK8PiSgSPJTX05TXhqqtUf4NVUQjcZKeno5ffvkF27dvr6v+RmFxdL6SkhKYTKZTjkF5Q25ubrj22mtF8QIqNnDHHXfgmmuuqQtbYxiGYRiGYc4R+YeAj0fC/tUMnDi4A/8zT8GnuBKHEF8rciSAykMJNz8JFD418IpQI6iTH/w8lZBJGq8arIcWOQjFYcRjo70ffi+OxJ7jme1qCNmj04ZQ6eiamhoMHDhQeHFI5DjLSJ/O+7Jx40Y8+uijosQ05ed06dJFVC9zQtXXSNxQgQISGVShjcpWnw7y2KxatUoUPZg6darwCCUkJOD9998X2y+55BL8+uuvGDNmjPAM0fGpspsrGo0GK1euFO0nTxK9p8+98cYbrdJPDMMwDMMwTCPYLMDmN2Db+CoOOWKxVTIfJQ5nbrYDNZZCyCpK4FFhhNJau5YSCShxoFRpQ4GfEdXBcki83GEsLYNGL4WnQQl3gxwmtQyVXjJY3N3grfaHt1mN6tBO7WoYJI6WTP7SRlRVVYmyxpWVlWJOGVeo2hd5HGjOGPIqtBdoHh0qrfzWW2+1dVMYpkna63eMYRiGYTo0hcdg/+1WHC0wYAOGoBS1KQgO2CCpKIC2uARS6z/zFcrUVigirVB422DNkcCY7waHrfHgLqnCBrtVCjhqi2aZ5XYhisIH9MP917yK81kbuMIeHYZhGIZhGIZpRzgO/YQTv7+KdfYBKEJA7Uq7FarifCgqiiGx2yHxtELTVwGFrwXe7oXwVlNCzt/0Bqw2GaqqAmAsdIOtSgKVvwRKLxPc3CvgptLDYlVCX+oPXboMVRlKdCrUIFR/6nyK5zMtEjovvviiCGNKSkoSeR9Dhw4V86Q0lXBOSerz58+vt06lUomnxAzDMAzDMAzDNB9H0nIs+/U77MbU2hUSB2QluVCXFMHhsAI97fDobEFUQBZk0n+q29odElSYg1FjD4a7NB1eqkr4+hQAPo2fRyE3wzsoD96Ubj0E0Bu8IFWZO67QofyQhQsXijwMyuV45JFHMHHiRFEJrGEpZFfIpUQJ905c5465UNmwYUNbN4FhGIZhGIZpT2RuxYbF72M3aK5FB8zmfPhmFEJit0LdrxIBCXr4av7x3FTa4uHjOwERQf0QGdQbCoWHWG+z2XE46wiOZ26Arno3pI5KWCSdoHCLgY9nHEL94pBTkoK8og1ws+9BpEc6tJpK5FjtHVforFix4hRvTWBgoKjuNXLkyNN+joQNVfZqLlTZy7W6F8XhMQzDMAzDMMwFS/5B7PzmGWx0DBFvJcWZ8CsphdzDBL+JFQjxLxPrzXYNpJqpGND9Ovh6xTd6KJlMisToXmI5LbFRACbAZnfgcHYuDqatQ88uiWhP/KscHUoAIhqbf6XhRJeRkZFickiaC+aFF15A9+7dmwyRe/rpp/9N0xiGYRiGYRimY1CahkNf3Ivl1uHirbQ0G9qSErj3rkZE/yKo5BZY7Er4h9yJPnHXQiarnVrEXlMDh8UCKNSwlZlgLa2BrcoMRZAGyk4ekKrqSwGboQZVK7bDsCcNUg8lvGcMg7p7DBIjw5EYOQ/tjbOuukaiZcaMGaLk8JYtW067H83HQvO9UGlkEkavvfYaNm3ahKNHjyI8PLzZHp2IiIgOVXWNYdoL/B1jGIZhmDZEV4TkD67CD4bBsEMGSVUBvEozETKhAP6h1bW72Ltj9KC34eURLd4b006i9POVsOQDUo8wSN0aq0zmgFRrgyLEDbCZYEovh8PuCYlMWX8vUxEU4W7wHNcb6h6hkMilHb/qGuXq0OSQTYkcYsiQIWJxQgUM4uPj8fHHH+PZZ59t9DNUrIAWhmEYhmEYhrlgsZqR9dVt+NEwsFbkGErhUXQSUbOy4OFrgtUuh3vA7Rjb6zbALkHV2iOoWnUUDnsAJLJ4yP8uyEbYjVVw6ItgN+sg8wqHVOMPu14OUypNsCMDJP6QyACH1QCpygi7CZDIfSFRBcJaDJT9kAGpag9Cn56F9sJZCZ3bb78df/75p/DMnM4rczoUCgX69OmD1NTUszk1wzAMwzAMw1wQFP76ML4rioEVCkjNVdBmZyByWo4QOQarJwb1/xaBvgmoOZaP0i8PABJ3QBL6t2AphyYxANph3SCR1MCur4CtzAvWkhJY8nJhzjkMa5kdDrMWkLlBGe4Oj9G9oB0+FFK5TJzfdLIAlUu2ouZ4CSSqMCiCbGhPtEjoUJTbHXfcgd9++01UDaNwsZZis9lw+PBhTJ36d0k8hmEYhmEYhmHqUbbx//D1MQmMcIPcZoBbegrCx+bAO9QAs02FxF6fIsAzDiX/24qaE1ZIJO6wm6ohlebDa2Z/uI8YXq/Ssb7CH8W2alSZa+AVMxxBUZ5Qe9QPU7PbHagoNKAsTw93HxWCooIQuOgSsa0mKQly/8COK3QoXO27777DkiVL4OHhgYKCArGeYuRoXh1i3rx5CAsLEwUFiGeeeQaDBw9GTEyMyOd59dVXcfLkSSxYsOBcXA9zDomKisJdd90lFoK+PCR6Z806Ny5MEtNjxoxBeXk5vL29m9yXKgBSu+geO9eMHj0aiYmJeOutt9qkXxiGYRiGacdQenxpKpC9C8jZBZSlA5HDgF5zAd/OYpfqpI34ev0x6OANJUxQpp5AyKB8+MdUw2aXIirmbQSaOyP3yVWATQuJRApb2RH4LxgC7cBaZ4Khyozj2/KQn1aJ4pPV4n1DPHzdEBjlATetAiU5OpTm6mA1/1NCmsRO5z4BiOkbiOCu3SCRSjqu0Pnwww/rDD1XPv/8c1x33XXidVZWFqTSf5KUyEi98cYbhSjy8fFBv379sG3bNiQkJLTOFTDNNshbm/z8fDGm5wOXXXZZi72E56p/zqd+YRiGYRjmPMGkA5bdBySvAGrKYYMERfBHEfygzPgR6g1fQR0UC0W38Vi8JQXl8IVKYoI8OQmB3YsQ3LtcHMYn9CmEpYahaM1BSCRa4cWRa9IR+uZ8SLValBfocXBtNpJ2FMBm+Ue0kHPHJ0QLrwA1ygsMwnNTXWYUiytypRS+IVqUFxqgKzfh0LocsWi9Veg9LgJ9JnRChw1da+lEmG+++aZYmPMTGlMKJ5TLW56u1ZK5kc415FF0ehXbmvOpXxiGYRiGOQ8gG/qPO3HyyHYkoztyEIo8STAsjtpcmDoKaSmlyVuEyEH6CXiHlSFsaJHYLPdeiLjywahYkwOJRAZryVH4zI6F19TbUZBeiX0rDyHjUAkVVBMERnqg66BgBEZ6wj/cHQrVP+cz1VhRfLIKRSerxWv/MHf4R7jDK1ADqVQCq8WG7GNlSN1XhMyDJdBXmGA1t68cnbavD3cubiSzvm2WFlTqJm/CokWL8MADD4h5iMg4fuqpp+q2UwgWhfcFBASIsnljx47FwYMH67aTB61haBSFTjm9bbR948aNePvtt0UoFS2ZmZlCiNLr5cuXC+8aVbejynlpaWmYOXMmgoKC4O7ujgEDBmDNmjVNXgMd5/fffxevqe3O87guFFLmLEdO4YyU10WCpHfv3vj555/rHW/ZsmXo2rWr2E4ha9Te5kLncQ1vo/aQt+brr78WoWUUXnn55Zejurq6yf4hqJrglClTRD9Qf1xzzTUoKSlpdltc+4UgDya1hcqf9+/fX2yjfQ4cOFC3z5nOeab7xXnP3HzzzeLzdK4ePXqIoiFOaJxHjBgh+pfKtdPx9Hp9s6+LYRiGYZizZNcnOHjkCD7HXGzFAJxEmBA5UrkEMrUdHn7u8PXxhEYhgQQOeEj1MOcnw0NVjYixeeIQRuVM9JfORcWSLBGqZi09gNDnLoap+wD88e5B/PLKXmQcrBU5Ub38cfG9fTHnof7oPTYCIV286okcQqWWIzzOF30nRWLIrC6IHRAEn2CtEDmEXCFDdO8ATJjfHde/OgLTbuuFboODL5wJQ89LLAbghdC2OfcjeYBS2+zdv/zyS9xzzz3YuXOnmG+IjO9hw4ZhwoQJuPTSS4VBSoKEjHQqxz1u3DgkJyefcYJWggx42peMXcqTIkg0OY35hx56SMxp1LlzZxFmlZ2dLUK/nn/+eSF+vvrqK0yfPh0nTpxAp05ndlHed999uOWWW+ref/vtt3jiiSeEYU+QyPnmm2/w0UcfITY2VlTsu/rqq0WbRo0aJc4/e/ZskQd20003Yc+ePbj33nvxbyDxRqKCjH0KoZw7dy5eeuklcY2n6x8SCyQqSWSSJ7KmpgYPPvig+Oy6devOqs479SP1LeW3UX6aM5fHSXPP2dT9QkKShBIJOernLl264NixY5DJZHV9MXnyZDz33HP43//+h+LiYlE9kRYKPWUYhmEY5hyRvQu5K97CUswWb3UohlRXBq9SI1QGC0hW2CUOZAdY4JEYi+EjZmL9R+/BT2dB5OwcKJR2VFoTMDnsfpR+dhwSqQLWkiNQ3TIFa38vQvqBYnFcyp+JGxyMPhM7CcHSmsgUUiGe2hsdT+i0I2gS1SeffFK8JuP/vffew9q1a4XA2bVrF4qKiurmEyJRQkY7eUFICJwJEkdKpRIajabRUCoy7slAdkLiibwsTmiOI0qoX7p0qTCGzwR5ImghduzYgccee0wY5iQkaPLXF154QXiInHMqkcAiDwMJOBI6lP9Fxvnrr78utnfr1k1U53v55ZdxtpDxT54eKpxBkJeE+peEzun6h8aAyp9Te52QMCAPCAkj8ji1BBI35L355JNPhJeFctNyc3NF3lpLz3m6+4XGkfqW7pnjx4/X7U997ISE5lVXXVUnsujz77zzTl3f82S7DMMwDHMO0JdA98NN+MExBTbIIauuQHDOSSFuCLtMAouHHMoKC4KLlMCqk9i3+m34OICIiXnQeJuht/pgTOc3UPrJMUikSlhLk1A2bgC2f5ZVG6ImAboODMKAadHwDtTwMHZooaPQ1HpW2urcLYAMV1dCQkKEuKEQNZ1OBz8/v3rb6Uk/PZlvDZyeFid0PgqF+uuvv0QyvdVqFeej4hItgfankDry8JBHgqA5kwwGQz1hRZjNZmHgE2SgDxo0qN5214lmzwYKWXOKHNf+bQrq+/Xr19eJNleo71sqdMgjRuPsKiQGDhx4Vuc83f1CUBgczWl1uvbROQ4dOiQ8ba75WSQGMzIyxCS+DMMwDMO0InYbrD/dgMX6AaiGByTmGqjzMqDq4gtppAds7kUI8MpCoKYEJ8vDUV3sD/MRM6TFQGCfEvhE68SEoD2i3oT+05OQSFSwlaXBNKk7dqytEiKHKqINmt4ZvqGt68HpKHQ8oUMlJVoQPtaW0OSprtCTfzI8SXSQEduwsAPhzEOhynYNi0NYLJZmn1urrd9HJExWr14tPEdUCpy8SnPmzBFipLlQvseMGTOEQHGGgxF0PQSJKCo97orTY/Vf9m9TUFsp1KwxTxKNybmgueds6nrOVIiBzkH5O5SX05DmhCYyDMMwDONCRTZQlgaYqv9edICpqva1mV5XAxVZWJ6lQTbC4LBb4V16DJGXZMLT13BKV0b65AA+ObDFSJFVHoFg39pwNA//B+D2gxV2qGCrzIZkfCds3GIRaeHxw0Iw5uq4enPlMB1d6HQA+vbtK8pxUyU08ko0BuWTUAK7K/RU39UYptAsqqjWHLZu3SpyPi6++OI6w7glxQBIdFHODRneVADA9UtH4VokaMjbQ6FSjUEeBQqTc4VC4M4ljfUP9f0vv/wi+v1sKtE1hELwKGeGwvecom737t2tfk7y9uTk5Jw2vI7OQTk7JGIZhmEYhjlL6AHj1jeBdc8Bjn8enhqhRCH8oYcGNXCDAWqUwhsH0EPYSNq8NESNz4SHrwE2hwwGRww8PAeia8Rw+HnF4ljGCuTm/w53aTKi/U6KYxrk09D7wAAYDZWwG6sg62rGugNusBiNCOvqjVFXdGORc8FVXesAjB8/XnhFKARs1apVQnBQ5a5HH31UJOkTlLxOr6loQEpKisjdaCh8yHCmxHX6PFXwasqbQTkbv/76qxBLFOZ05ZVXntH74QqFvVGeCOXckEgioUYLhb9R+Bh5jO6++26Rt0PhWPv27cO7774r3hNUyICu4/777xfhXpTb4qzYdq5orH+oGEJZWRmuuOIKIUiorStXrsT8+fObLRpdcfYj5VVReB4di7xmhFMMtsY5SUCOHDkSl1xyifDMUTgaFbJYsWKF2E7FDegeonwrGmPqa5r4tzn5VwzDMAzDkPIoA76/HFj7DMocHtijHYslmivxvuJWvITb8Dkuw4+Yjj8wAWsxXIgcQlWci059kuERaECNVYu+/VZg1vhlGDfwKUSEjIdGE4n+3W/GzPHLMWjgGii9b4bEfR5GWBfBmFQJh90GS/5y7DB3F3Pe0Dw4k2/uCZmczfgzwT10HkIGMJVaJsOVjF16Qk+lkaliF5UOJiZNmoTHH39clBumUtBUbWvevHn1jkPigqpukUeFPEBN5du88cYbovra0KFDRRgVHZ+8AM2FSjWTwKHPU7iVc1m8eHFdcQNqLyXFk/eGKoBRKBuVm3aGT5FXgwouUFEEqs7mmpx/Lmisf0JDQ4V3iwTGxIkT0bNnT5HATyGDrhPhNhcqDf7HH38IcUElpkmsUjU6wpm301rnpP6je4EEE10T3RtOoUQeHxoj8vhQiWnKjaJ20LkZhmEYhjkDuXuBj0fBkbISq6QD8Tbm4099b+w3BKHYQn/PJTBLjKiWlqNCWoQSaQ5KkQlVbhpCAo4jIL4CdocEkTGvwt+7M+xGK2qOlqD8txQUfXAAFX+lw5yrg1YbhRF9H8AQrzugW5UvTm089D2SB1yOwoxqqDRyTFvYC27a+uHsTONIHM2ZBbSNoRK9VCWrsrJSGI6uGI1G8fSaDGauHMW0B6ggAAlYup/Pl0lOm4K/YwzDMMwFibFSlIZG+gZg1//BarPiW+VMZJhr0wqkBh3khmrIanSQ1ughtVlPOYQmsAZdZmRBJrMD7jdhkO0aGA4Vw3yyGrCfaoLLA9RQ9/CHbnseHEYbzBmbkBYWjmRDhCgfPX1Rb0TEnXmakY5OVRPawBXO0WGYcwyFF1KpZyrEQGGBzjly2oPIYRiGYZgLioLDwIHvgMwtQOGRujwcPdT4P+U8VJo9xATxyuIcePhrIVO6waGVw2HTwmGxiVxbqUwBiUwOh9KCkAEbhcipdAzF2Iq5qFjnUj1XYoC9Kh220nQoOw+CQxIEa3ENqtdni8220lSkGquRYogQ70df1Y1FTgthocO0C2gyzM2bNze67ZFHHhHL+QrlKlGYGP1L4Xw0GSzN5cMwDMMwzHkABTeR12bbO0Ba/cnBHd7RyPYfha8zvWAxSwGbFb6OgwiZmgtfTWWzDl9pCcXYgGeg+zFHvDcl/QFL1nY4DCV1+xgP/AnI3aAZNAOKqKGwZOUiI3MPUrrMEduHXNwFCcM43LylcOga0y6gSTapsEFj0GSntDDnBg5dYxiGYTosx5YAm16t9eQQEikccTOQEzoGm/LNyEjNh9VcG2JG8+AEB+5EdI9cyKTNK9hksAWhf/h7sH1rAKwOmFNWwpS3ETXdR6AyIB5l0iBUGuTwNuXD59gq+BcfhNxmQrFfLxzucaNoT+KEThh2CVdNdYVD15gORcP5dxiGYRiGYf4Ve78A/rhTvHQoNDgUNxMrSv2gO26F7LjLFBt2G5T6YoQl7kN4WKlYpZeOx8TBT0MqVUIiocJBVElV8ndFVUndOnsVUPzBwVqRU3AYu+VBKOv7DOw2B1D+zylqEIL8uGshjbfDT5eGUnWUEDlxQ0MwdHYXHuizhEPXGIZhGIZhmAuLk9uAv+5DskKB30PGoCjPH9pD/pBIZJBBIebLkesrAWsZFKEGRPXPgL97JWx2KbQB92Jsr5vPOIeN3WxD2dcHYa+2wFadh70GB0p8uwM2BzReSoTFeiM01hvewVrkHC9D6r4iVBbVoFgbKz4f3dsfY67iuXL+DSx0GIZhGIZhmAuHiixg8TVYL9dihXEm1Fm+cKf1EkBm1sEvIB9uwQ5I1KUI0mZBJTOLj+mtXujZ/V1EqPuh7NvjsFWZIVHKxCJVyQCZBA6TDXa9BfYaq9hOrx22GpzIPo6i0JGQK6SYcWcigrt41RNK4d18MGhmZ5Tm6pG2rwg2ix0Dp0dDKuOZYP4NLHQYhmEYhmGYCwOzHvjhSuQYdVhjvQpqqaeorKZFMUK7lsHdLwnuSl29j5hsGljk/TFu+IuQZStR+Ol+OGpOLSXdKFI78g6tQEqni8TbMfPiEBLj3eiuJHz8w93FwrQOLHQYhmEYhmGYC6O62u+3wlhwDB9KroBC6gkvbS7Cu2TB2ysNUklt0YEqsxd0jn4I9O+P3p1HIMg3DnBIULXmJCrWpwAOQBHhAc9R4bDpamBKOwlzZjasJeWQuasgD/SBIjwQch8tkp94Dvu6LRDHTRwfga4Dgtu4Ey4sWOgwDMMwDMMw7Re7Hdj7OaD2BhJmAVJZ456ctc/AfmwpXpfOhsIeAD/fDCT02FK3S2Z1HHwCLsfkYZdCo3KrW2/TmVG2+ARMKRXivaZ/AKz561DwzEYYjx4FbLZGm2WRa3Cg34Owy1QIj/MRJaKZ/xYWOkyziYqKwl133SUWp4v1t99+w6xZs85JL27YsAFjxoxBeXk5vL0bd/OeLf+27ef62hmGYRiGaSZb3xQiRuD/EjD6ISDhYkBK895YgH1fAhtehkNfhE+kk2Gxd4JGXYaucdvFR1IqByA25i7MHz0IUmlt3oy13IiaY6UwHiuFKaMKsDsgUUjhPtwLpR88hIqTxTBogmH27webfxgcYV1g9Q6EUW9DjcEKk0UKk0QDu0wJDy85Ji3owfk2bQALnQ7M6NGjkZiYiLfeeuucHD8/Px8+Pj5oj7TntjMMwzAM8zcnt8Ox9nmcQGco5XJElyRD8vP1QOBrQO/Lgb1fAmVpqIYWfyovRr45CnKZEQnxayGX25BRFYcrJ38OT7UaDosdut0F0O/IhyVfX6+LFWHuUAYXIvfRe5EaNA7Zg8aI8s91UL2ColMtbLWHAlPvSISbu4KHrA1goXOB43A4YLPZIJe3/FYIDm6/cabtue0MwzAMw9BsnGXALzdgP+KwFBMBK+CvuQgDzVvRu2gfVKufQBW02Cyfgj22bnCYyVtjR3znNVC7G1Fq9MWIgR/C3SFH1fos6Lbmwa6z1HatBFBGeUId7wdVjAfKPn0bx77dh6Tu98Co9he7+IVpofFUQu2hhNpdCTcPBTQeSiFqaD396+6jglzRSCgd858g7YiGu8FiaJOFzt0Sb8uiRYvwwAMPwNfXVxjeTz31VN32iooKLFiwAAEBAfD09MTYsWNx8ODBuu3XXXfdKWFTFFJGx3Vu37hxI95++20RZkVLZmamCAej18uXL0e/fv2gUqmwZcsWpKWlYebMmQgKCoK7uzsGDBiANWvWNHkNdJzff/9dvKa2O8/junzxxRdiu91ux4svvojo6Gio1Wr07t0bP//8c73jLVu2DF27dhXbKWSN2tscqN+pn1yPR56skJCQuvd0jXStBoPhlLabzWbcfvvtYn83NzdERkaKtjpJSUnByJEjxbaEhASsXr36lDYcPnxYjBG13c/PDzfddBN0Ot0p4/XCCy+IPqZQvGeeeQZWqxX333+/uAfCw8Px+eefN+uaGYZhGOaC5u/CAsVVNfgDY/9eZ0eJwYFl1qF4VbYQ3ymvwJuSG7HbGgeHQwKpQYcYn3XwDimHyaaAZ/CrCD5sR/5Lu1G18qQQOVJ3GWSeOYBtJQybX0XeU9fiyPQZ2LZXggOJi4TIIfFy0R29cfnjgzDjzj6YcH13DJ8bi/5TopAwPBSdEwMQ3NkL3oEaFjltTIfz6NRYazDou0Ftcu6dV+6ERqFp9v5ffvkl7rnnHuzcuRPbt28XxvCwYcMwYcIEXHrppcJoJkHi5eWFjz/+GOPGjUNycrIwis8ECRzat0ePHsKgJkgMOMXDQw89hNdeew2dO3cWIVzZ2dmYOnUqnn/+eSEIvvrqK0yfPh0nTpxAp06dzni+++67D7fcckvd+2+//RZPPPEE+vfvL96TcPjmm2/w0UcfITY2Fps2bcLVV18t2jRq1Chx/tmzZ2PhwoVCJOzZswf33ntvs/qRRAsJERJxc+bMETk9x48fF/2XlJSEuLg4IfpIvGk0p47PO++8g6VLl+LHH38U10ptocUp0KhdJE5onCorK+tylJzo9XpMmjQJQ4YMwe7du1FUVCREKoknp9Aj1q1bJ8QMXfvWrVtxww03YNu2baLtdOzFixfj5ptvFuNP+zEMwzAMcxp2fABL8mp8IbkaDoccMl0V1LlpsHj5wewTCKvKDcm22ugNmaEayuJc+McWIaRnvliX67gLc4siUb2h9u+9zFsGa+F2ZG7aggL/vqhRh8Ho1gumGB84XIob9BwdjsGzOkPp1uFM6A4Jj1Ib0qtXLzz55JPiNRn/7733HtauXSsM9F27dgmDmUQHQaKEPBDktSAhcCZIHCmVSmHYNxamReKHDGonJJ7Iy+Lk2WefFcn2JADIYD8T5AWihdixYwcee+wxIeRIaJlMJuHJIA8RiQGCBBZ5WUjAkdD58MMP0aVLF7z++utie7du3YSX5OWXX262h4yORZCQ6NOnj7huEj8kdOhfOk9jZGVlif4fPny4EE3k0XFCbSaxtHLlSoSGhop1dC1Tpkyp2+e7776D0WgU4lCr1Yp1NJYkFKn9JJKcfUyiSiqViut75ZVXhIfpkUceEdsffvhhvPTSS6JfLr/88mZdN8MwDMNccGTtAFY/icXS0dDbfeEmq0CPYavgcPNFdZYdBVvzYZH7wKbxgExfCd/QUgTONsJdmyc+fqRyKhbEXILyb5PEe7tuB9K27sfJiAmo7nXHKaeTyiTwj3DHsDmxCD3NHDjM+UmHEzpquVp4Vtrq3C0VOq5Q6BSJGwpRo7AnCoFypaamRoSYtQZOT4sTOh+Fn/31118iUZ9Cquh8JAJaAu1PIVrk4Zk7d65Yl5qaKgx6V2HlDBkjQUKQB2bQoPqeOKcoag4kYu68804UFxcL7w0JH6fQcXpOKEywMciTRm0j8TF58mRcdNFFmDhxYl27IiIi6kROY+2ifUgkOkUOQZ458gaRR8wpdLp37y5EjhNaT0LQiUwmE2NO9wDDMAzDMH9jswI5u4HkFUDKKqDoGLZJY5BqJzvKjriYDVB7majUEDSx+fDvooCxUgpDrgHa6BJotLVeHLNNgcPlE3H9oCdR8b8TYl1Z/lHsRicYu/f7+28xEDc0VEzq6ennBg8/NbReSkj+rsbGtC86nNChJ/ItCR9rSxQKxSltJ+OYRAeJHjLSG+Iss0wGc8OcIIvl7wS6ZuBqlBMkTCj3hDxHMTExwqtEYWAkRpoLhXDNmDFDCAFnuBzhzFUhERUWFlbvM06P1b+lZ8+ewmNCIocWCsEjoUMeFQono74ZOnRoo5/t27cvMjIyRJggeXBIoI0fP/6UHKJzMd6nuwcYhmEY5kLDXlWI3V89Bkd1ASJlxQiSVkDoC5MOMFfX7Zcr9cQyxyRhxEb67YBHUDWMVhX2lc1AhHorIjzyoPVJgvbv4qokcPaXjkZi/O24d0Qcij88CIfJBr2+DFvUXWn+T6jUUvQc2wm9RoeL4gJMx6DDCZ2OABneBQUFohIazV3TGJTbcuTIkXrrDhw4UM9wptA1qqjWHChnhDwbF198cZ04aW4xAIJEF+XckJH+9ddfC4PdCSXwk6Ahb8/pwsfi4+NFmJwrFALXXOh8I0aMwJIlS3D06FERhkZhexQ2RyFt5MFqKO5coYIPl112mVhI4JFnp6ysTLSL8nXIy+UsbtCwXbQP5eKQ0HOeg/rTGaLGMAzDMB0ZephIS2N5sM3FYTXjr/97Gnt19EC09qGoDCbYFYUoU5XDqlRDZveE0qKF2qKBHDJ4KrMRHl8b6ZJpuQWPX74IqUXV+GPPWhjKf0Unj1Rk6noiLuZWPDC2F2RSCcq+T4K10ACr1YjNVg8hcvpPjkDfKZ2hUHF1tI4GC53zEPImkFeEQsAoj4MqkeXl5QmPCAkRMtqpwterr74q8kJoX0r0J+HjDAUjSCRRkjsJFsqfaaqIAeWo/PrrryKvhETD448/3iLPAoW9kTdk1apVQiQ5vTiUK+Th4SE8Rnfffbc4JokQSuonMUAC49prrxWFDCg/hyqQUSL/3r176yXyNwcKV6MCBtQ/znwhSvSnwgh03NPxxhtvCBFDfUfi5KeffhLeIPKe0VhQ/1Mbqb+rqqrw6KOP1vv8VVddJXKtaB/qBwqfu+OOO3DNNdfUha0xDMMwzPkMPRikojn0N4wiIChv1vWhZWPQ33qKmqC8YooAIRuCqp6eDWs/ewZ7dYFwwIEKdTncjR6AQwVYOsHHcmpRJLldh24J28ScoMfLB+H6STfDYbMjJtADd0+dBaNlOlKLdJgb5A6VXAa72YaqNVmoOVQCh8OO7QYpTA6gzwg/DJoVe1ZtZs5/WOich9APC5VaJoN6/vz54keHDG8y2p2GM1X5IjFCeSeUCH/99ddj3rx5IoHfCYkLMr7Jo0L5NhSe1ZSxT8egHzd/f388+OCDwqhvLhQuRj94DcPDqFwyeYqouAF5oaj6Wnp6uhAR5LlyJuJTtbNffvlFiKF3330XAwcOFEn/1KbmQt4i8mA5S2wT9Jq8PK7rGkJCjAQllZGmPBmqzkb978ynoaIMlOdDbSLxSAUFyOPjhJ5gUbECyhFyVna75JJLRJ8yDMMwzPlO1smT+O2n71CuozwXiL/T0ZHhGD9xyikh50RJSYmoFkuRJK6RI1Q0qbS0VEwR4ZqTSlBFVDou5b0GBgbW27blpw+wJb/WJE3WHILRnA+pXI4It2gEyEKhsmoh1RthLSmBQ6+D1GxC5LAMuHmaUVrji/Gxz6L8tb0ohwSanv5QJwZAFe2FHmFesOnMqNyeA/32PNgNVnGOowYLyuxSxMbIMOTK+vnSTMdC4mjJ5C9tBBnc5BkgLwB5AFwhI58MeJqfheY5YRimdeHvGMMwTMfEajZjxc8fY09yiZgh0ywzoNQtHyF6CpuvDeOKj41GeFSMEDckYkpLSqD/e046IgyFGIrdKEAANmNQXfEdikqhcHoKA9+8ebMotOSMFKGHhvRgkKqiHtj4B/7YdECsz5OeQLej/+TiNIbaR4XggTJ4Re2F3SFBufwNjNwbBmvRP20ipJ5KqDp5oCapHLD+HaGikeBQoR4ZNgVCPPWY9eI0SGUdbkrJC4KqJrSBK+zRYRiGYRiGaYfQs+pjZcdwoOgABgUPQoxPTLM/u2rbZ1i3/gjcLF5C5GRpTsKsS4e2AtgWkY4wSyw66SNxPCVDLA3ODKU8C3b1UaSoSrBbpUaIKQmTSiuxBhNEriwZoFRJ9NChQ3XFkwJRjGL4iZB6EVbvpoDeSEWPJMhSpSLuUBVkKjvCuodB6ZrvI7dCFmiATZsNrSIJEknt8Y5WXYJ5jngYigoANxnkwwKgqgYMh0pgrzKj5kip2E/q6UBhfjJ2lQTDKtfAB6W46OlZLHIuAFjoMO0CmreGngg1BoW/OUPgGIZhGKY9cqLsBL4+9jXMNjMUMgXkUjkUUgU8lB7o7NUZsT6xiPaKhkqmQklNCf5M+xNL0pYgtSK17hijwkfh+h7Xo29Q39Oex2Kz4M1lj6DsgAxuNi+YpCbUIBUJxy2QGDzEPl1zgJNBWdgdlYFAezRkDhmqFdXQKXR1/1qlVigsEvhU+8KvVIlyeCEzqASPFvyMX6UXIycnRyxEDDIxGDuR51EDN5MUleauOICe0BmpgI8EWeoMRB8phWeACdEX5UAuT26yr3J1IcgzDcX8qDtg+CVDFBTYXmJC8S/Z8KtOQaw8A8ER4ZAo/FF+cBsOyruh3DdeWL3ejlLMeGoslOr6VU+ZjgmHrjHtgtzcXJFn1BhUZKGpQgvMv4ND1xiGYc4ta7PW4uHND6PGWv/vnMwuEyLDLKud6kEqkSLMPQx5ujzYHLW5MUpIkSBR46BDLwx+IjEgEVclXIX+Qf3hr/avO15WVRYeX3oPQtNjobKrIJHoEFhSDkNRodjuFRSMwMjOSNm9ndxFYl1+sBm5vgZ4ONygdbhBY1dBbVFAVW6BtKI2p8dJeogeqthcPFhsxp/Ki6GVmjHCuBqQl+COTjFIcdRen1wiRVdo0bXUHUelbuh2pAbBVim6XpIFpcYMnVkDs/2fEs82hxR5+s5wKAehc/hoDOuWAF+bBIVv74OjxorUGjOOmuoXTvCozoJv2XFkh4+GXaaCVGJH/1F+6Hdpb/bkXEChayx0GIZpEhY6DMMwjUMhWTm6HCFQ3GRuwtviJncTE4grZcpmff5/R/6Ht/e9LaqNDXPriV6mEBgqrDDpFLCYaz0eZkU18tVFyFEXoNitWHy2v8ETA6qU8DN5wQBPeEnzsN87H0u9AEud5AFCtCHo4d8DnTw6Ydn+P9A/bxCUdiWUNh2UaSmQ2GzQeHlj4OxLUOCVC11NEXoFT0D6mh1I2rapTvCcDnc/fwRERCLz8H5R9SwzWI+Q2Dw8XFIAkh57PP1wv18Aoo4rEZPrAb27HSf9dMgLqEGplxkjD/ijc6EGXWZmwz1Qj3x9KHr2/hE+Wi/RJ3YHIJdKEOathvTvSTsddgeKPzkEc0YVqqwWbNABXjXZmPTgWBzakIPkIwbY7P8In5Aunhg7LwHeQe1jnkXmzLDQYRimVWChwzAMUytKKk2VyKjKEDkx+4v242DxQZQZy07pHvK8TI2eijv73olgbXCj3Uchak9vfxpL05ZSygsuLR8Ee2X4GbtaAvLkSODAqUn0EtgRgSyUaNOxw68aaVIbJA4ppA4pvMxeGFI0RIgcjU0HaWoKpA4HBl0yB9XhZTBWfgMPZbk4js0uRZl9LBLDr0LO9qMwVleJnBmlWgMVLRoNfMLCIfc1I7VoB8rLt0FirED2UhkslXKcDDKgZ48q+Gj98WlROQYd8oanoZFQMYWMYukQPqIA/gnl0FvU8O30NYbH/zNVhiskcMzZ1dDvzIdhXxFscGB9lRVmkw6zF0TCf0hvsV+NzowjG3OReagE8cNC0X14KCR/iySmY8BCh2GYVoGFDsMwFyJHS4/i00OfIk+fJ3JiSNBY7bXliV1x5tEYrUaYbKa6kDKCvDzX9bgO87vPh0ZR602gsLONORvxW8pvOF52HHLIcU3xYFTpaqeP6KQ2INxbhfCQQIRHx0Kh8UJGWjLSsvKQVlyDClOtwNGq5NC6qWGvqkBNaTHgHwx9M6a/87TrYE9JhhQOxF7RHVblWngqa8Vauckb1ZYQdHI/Lt5TVbNS2ygE+g+B2VIFi6UaFqsONksx3BwHoJHXr5CmN3nh5O+BMFfIkR1ggMHNhm7ZtXk/nmFaBI4Og8rhA1OGBVkHkmCuMcC3WwU6jc4X5ypWPIdLu02HpchQO4cPiROpBA6TDcbkchiTymDXW+rOt09vRbbFgVHxJehx59yzGWamncJCh2GYVoGFDsMwFxp/pf+FJ7c9KYRLQwLUAegV0EvkwSQGJiLBL6FemBqJoeOlx/Hantewr2ifWBeoDsT4yPHYVbCrXvEAL5knrsjtg+Iaf+GNmTYgBkFRiSjOOomSrAyUZJ1ETXUVVFp3qD09oXb3gF3phtKT6ShJOzVh365QQRoaCbOnD0zWfwSXEz/oYUo6AanUjvBLHfDzShLrK0xe0MmvxIyht8Jbo8H6IxuQnv4uoj0ONtlPRqsSWbpusCr6wl+yHIGaIuhNnji5NBDmMqcHx4HISSFQh2+Hm9xY206HBJWWLpDZI6FRbIBcZsMJ/eW4LvIulH1X26bTIXGTQRqmwY79hcizKxAjOYGJH9xyxslNmY4FCx2GYVoFFjoMw1wo2Ow2vLP/HZE3Q4wMH4nLul0GPzc/+Kn9xL9UEc2JsTwfR1d/g6KySlSbgGozUGWRwGyToEfXKCAxCO8eeRe5utx6YW19AvtghG8/2NakIM/sCzms6BXgjtQt2+D4e66ZMyKRILRrPLoMGITAqCCk7z2KYxvXwWSoLUrgkMmhUqvhFRAIn4Ag2GxWpO/dBYp4iyCR450Ei02OHNt1mDl0IQIaJHRTqN6GI5uRnP4ZpKiGzaGFQ6IBpO6QSj0R4NsX/WNHopM/lacGDp5Mw5FD1yJYmw+DWYusZWGQyb3gP6YGPtojYp88fThkEjOCNEX1zpVW1QdX9PkUVZ8ehcNihyJYA4lSVpseREk6EkAZ4Y5KXQmSDucgq0wDi1QN75psXPrmTCh9vc9+0Jl2CQsdhmFaBRY6DMNcCFSbq/HQ5oewKWcTlDYlLpVPRmCxHAG+HojpOQjRPQbUTUxenHEMu1Z8j4OFNphx+qIDapkNI0cMRYp/MdKr0jEgeAD6auORu3UZdh1NQ5HNC0qYkeCjRdq27eIzXoFB8O8UBf+IKARERkHr4wuTXoea6mqRK1Ojq4ZnYAAkIVKkFq2D1LQRHooSVFmC4el1EXwNXXBs7Q4UpDZSolnq+FvknBAip8b9eVw8ZA7sZhsMewuh25oHm84Mbd8guA8Pg9y3eROxO6x2kT+TVJSDvfvnIVSbA4PFXQgUjVwHq12GDNMVuHrcI1DKFdiTnoSkzPUw6HaQksH0Aa9A+W0ubBUmyLwtkMoPwGE2QWcAqmqUKNUpkG0KRo3bPxXk1MZiTF8Qi4Dhpy+lzXRcWOgwrQ7NZHzXXXeJhSA38W+//SZmPz4XbNiwAWPGjEF5eTm8vf/d05rRo0cjMTERb731Flqb6667DhUVFfj999/REWGhwzBMRyanOgfLM5bjl5RfUFVWhW7VXRFd3Ql2R/1kfykl+ntQCQA7Mqr/8er4y3ToGuwOT5UUHioZtCopjGV5WJflQBFqDfMgrQQ9e3RH6vGDOFn1TyEBtcSEWHc1MnbtEiFeiZdNhDVcg/LqZNhM6VBLs6CW6WC0aWG2u8MGTzgkWmilyfBQVDR6PRQWZpD0RZew6fCShcFYoUB1URkqSwpQ5LUM/h5HYLHLUKN9AdMTpkO/LQ+6nQVwGBvkH0kBdc8AeIwIgyLMnWLyYK+xwW60wq6zwFKghzlPBwsthQZIZFL4XROPk1oDduy5BuHuJ8Vh8vRhCO70Iib0HtZoe6lSW8lnR2BKrwRkRhw6tAklnl2g1wSJstCuyGwmhCqLEdvbG11mDoYyKPBshpzpADRX6PCEoR2Yc2ncE/n5+fDx8cGFzttvv1036zPDMAxz/kPV0/5M/xPLMpbhUPEh+Bn9EFcRh+Ca2gppFDwWjCIkBgJlBhtSdWqUwRsnRe69TOTTdFZWwEvji/zkEhw5llwv5EwikSJhQCJ6SzKwuSoEhXo3FO6k8C2Z2B4qK0dcVAhKCqRC5FBFsNBLVID3O5DrgABKN3FxpijFPDq1FdGc1FjdkFvTF54+ExETNgT7kpdBYlyGLl4pcMdeFObuReHfwkcnC4YtUAF/ZVadyJlgH4KCl3fXhoaRQejnBvdhYZD5uUG3JRemlArUHCwWC2QSmsymyT512Gwo+fIYIq9NgGLwd/hzyyOQyENw2eiHEOjpAVNmJfR7CiH3VkEZ7QVlhAekShkq/8qoFTlSO46cOIiMsDF1x5RKHPBQmeHlbkd0nyB0ndqbJ/pkWgQLnQscMtBtNhvk8pbfCsHBjZfMvFCgfiOvFj1RYBiGYdoH+wr34d6N96LEUCKEzeiK0fAz+f291YE4pCHR14osWxx27jgMd29fREf6oafGAZOxDEaLDUa9HLmHMlDkSG/0HA6HHUd37YO7jy8uGhOI7LS9KDEr0SVAjfghU6BTRmLH7z8i6/AByBRSBM0GArwPiLLOuYY42GXRcHePRZBvHAK8wlCpL0WVoRR6Or+pHJ7ukRieMAU+7loRakYelX5jb4NZuhDL9+/GiczF8JYdRqh7AbSKGngq8kW7akXO8xinH4CKNbVtV0Z7wmNEOExuFuz8dAP0VRYMuLIPAqf0gW5TDgyHiv8RORJAopJDqpFDEaiBIlQLqcYK4+HtMBd4w2HyRsmXRxE6rzsWza7Nc6Kcm4plGdBtyq49gBOZRBzDkq8Xb3OT1iMtcKQYg1FXxiG8mw88/d14ck/mX3FqEfYOYLjbDYY2WVryVJ+8LYsWLcIDDzwAX19fIRqeeuqpuu0UCrVgwQIEBAQIl9zYsWNx8ODBeuFSDUPGKKSMjuvcvnHjRuFtIGOclszMTBEORq+XL1+Ofv36QaVSYcuWLUhLS8PMmTMRFBQEd3d3DBgwAGvWrGnyGug4znAtarvzPK7LF198Ibbb7Xa8+OKLiI6OhlqtRu/evfHzzz/XO96yZcvQtWtXsZ1C1qi9LWHr1q3i+jVU39/HB5MmTRJhb06oDafrb+KNN95Az549odVqERERgdtuuw06na5uO10LhdAtXboUCQkJou+ysrJOGQu6LjoOXYefnx/Gjx8Pvb72h9y57wsvvCD6mo73zDPPwGq14v777xdtCw8Px+eff16vbQ8++KDoG7q2zp074/HHH4fF8k+JTYZhGKZp6G/090nf44aVN0BWKsOUgikYVjhMiBwZrOiHQ1josRaxnRKwZr8Dh3fsgdVkQkVhPpJ37cS+DbtwdEcq0vZmIDcpRUyk2alXAvreMAgD7hmD4Q/PwcRnbsHsVx/HjEcegE9IGHTlZVj56zrYtb0xbu6dsPkMxE+f/oofn31EiByFWo6gS2wI9D4q8lhq3J/G/Bl/4IZp72DuyNsxLGgYOuuC0FfZE2ODx2Nm3BW4fMAdmOAzDpJNRSh8ex/yn9uJkk8OI//5Xaj5KwMXRfbEQ3PfxHUzViKhz3ZIgv9AifIVnLTeCm3w/2FMaT9UrckSfeI5IRK2YX5Y8/l6fPfSYSSVBSLbGobfvszH2pd/hmKgO0IfG4zghwYi9KkhCHt+OMKeGoLAW7pBpj2Gim+eRPaNM5D98cfI/+NN2HVpgNWBkq+OouZEmQhtK3xvvxBMJHIsObvE4jBXCfHkFDlVJ7dgn/8Q8XrARdHoMTJMTO4plXU4M5X5j+lwHh1HTQ1O9O3XJufutm8vJJrmz7r75Zdf4p577sHOnTuxfft2YQQPGzYMEyZMwKWXXioMZRIk5DH4+OOPMW7cOCQnJwtj+EyQwKF9e/ToIQxpgkSTUzw89NBDeO2114TRTKIgOzsbU6dOxfPPPy8M+K+++grTp0/HiRMn0KlTpzOe77777sMtt9xS9/7bb7/FE088gf79+4v3JHK++eYbfPTRR4iNjcWmTZtw9dVXizaNGjVKnH/27NlYuHAhbrrpJuzZswf33ntvs/vywIEDon+uv/56ce3koVq/fr3wujSnvwmpVIp33nlHiLH09HQhdEgYffDBB3XHMBgMePnll/Hpp58KERMYGHhKON8VV1yBV155BRdffDGqq6uxefPmeiJ43bp1QsxQH5A4u+GGG7Bt2zaMHDlStG3x4sW4+eabRbtoP8LDw0MIrdDQUBw+fBg33nijWEftYxiGYZqG5rh5dsezWJq6VISoda/oLtZTIYD+OITBXqUojZiNP7emomzXNrEtKD4I/kP94KEKhLTCHSXp5SjMSIPNakXsiAQYg7IB2zLYZTWw6wGLHqBHY856Ym6ThqB36UQc+nMNkrZtEosTmnizy6D+qAhbj0D3ZJhtCkh8XsCMvheLEK+aY2UwHiuFtaSmWUMr1SrE/DL6HfliodAwbf8gRIa5o0vXOEjkCaJYQMVvqdDtrq0ApxgahJV/7kdRDc1z4yseffuac6FUSVGAEKSbOiHrhQPoEVqOmEFhMKZnwphxEsaT2TDkl6JKE4FKr96oHHIxzMraHInYpF/QrbMJMt8ElH51rLZxNgfsxioYDn4LQ88eUB3bCumeTyHR+EPTfwKMZVXY7j4IdqkCkT18MWBqNN/OTKshcbSD5IKmEo4aJkqTZ6UthY60mUKHPA9khJMR7GTgwIHCc3PRRRdh2rRpKCoqEqLDSUxMjDBsSQg0lgBPHh0y+Mlrc7ocHWeCP32OPDhNQSKJxMvtt9/eomIEO3bsEOcgYTF37lyYTCYhzshDNGRI7RMbgjxWJBy+++47PPLII1iyZAmOHj1at53EGImK5hQjuPLKK4V3hbxTLe3vl156qdHPkGeGrr+kpES8J6Exf/580cfkkXLiOhb79u0TnjISlJGRkacck/alMSAhRcKKiIuLE4KJhA9B7aT7ncTU5Zdf3mjbSKT+8MMPQhCea7gYAcMw7ZliQzEWrl2IlJIUDCgegFBDqFjfHwcx1qcQuSGzsWt/LgrSU8R6jY8WwZPU0Hhsh1z6T4K+0eYFm7wHHHYj3CV769YXGgJQauoElbRaFA7QKqrhodSJ/BKdxQNq1Q0oWJ6CspwsRCX2R7dhw1HgVoCC/E8RqM6A0aqCJvgNDFYMQPmvKbBXW+qHdwVr4bBQEQCbKBjgMNsh81RCFesDt1hvqGK8hdAxpVVAvz0fNcdLa5OMnEgBua8aEpUMllydiB6TDwnAH3+mwizVQOKwIdiUhj4XdUXUJWPE3/a0v/Zg6++ZqJad+cEqQTlGJKSITlmr0TMoFPKgRPHekrcfZcmrcXzwrag0ukEiBYI0Ovgc/BP++XtxJOF6lPvGw9NPhbmPDoRK80+hB4Y5HRdsMQKJWi0ER1uduyX06tWr3vuQkBAhbihEjUKmyGPgSk1NjQgxaw2cnhYndD4K5frrr7+EV4JCqeh8JB5aAu1Pwoc8PCRyiNTUVCFonJ4TJ2azGX369BGvjx8/jkGDBtXb7iqKzgSJD/KCnU1/OyEhRp6npKQk8QWiPiAjn9pOIWOEUqk85TiukAAizxKFrlHo3MSJEzFnzpx6RRu6d+9eJ3IICmEjUelEJpOJsXdtG3l5yNtE409jRW1r6ovNMAzD1PLCzheQXZiN8UXjoTVrRZjaVMc6uAX3x+KUIJRtq304KFcqET4hCPKgnXBXVIp16ZUxkMCKCI9suMkqAcdWIRQowT+pvAfcvC7DjBEzEeT1z0NOu92BbUlbcTL9EQRrcgH7W1COHYrxsXfiWMafSKm+E1qjDoFqwGBRwy/iPfSR9kLp18eE94MmxFTH+cItwQ9u3XwgVdU31YSgoFyZvyfIpPNVlxrh3tkbbjE+sFaaoN+ZD1NqhcjdcZhs/3iGZBLIBvngjz9SYJZp4V6Tj4mXBCN4+s31JtzsMq0/oib3xYHP1mH/nhqYJBpIJXZQJJlMIYNcJYd/lDdCunghuIsXAiM9cHBtNnb8no6sThNgKdiJnsU/w2EoRYbMG+l97oLdKBH5SDaLHQU6dxR0uRzocpkIaZMpJJhya28WOUyr0/GEDuWGtCB8rC1RKBSntJ3ySMiQJSPc6ZlxxenZIEO5oTOuJTkblIfiCgmT1atXC08BeY4obI4MdBIjzYXyUGbMmCEEijNcjnDmuZCICgsLq/cZV4/Vv4Hae7b9TZAHhjxpt956qwjfIw8UeYcorIz6wCl06DxNzb5MIoX6kULRVq1ahXfffRePPvqoCEkjr+Pp2tFU2yjM7qqrrsLTTz8txBM9wSBvzuuvv97s/mEYhrkQOVB0AHtT9mJMwRgo7Up4QIeLbX9hR0Uv5CfVJuOrNBqEjY2E2Xc/vNW1ubDFNQEwa27H1dMuF7/Hh7ILcfTkHpSW7YHDYUJs1GzcNKo/lDKpyDMxlVcKr4pMq4BELcfwhOHoFb0CP65/DhHKnxCg2IbCzG3woz8fCqr65okqyTgM6bkAkYYgkcBPIkfd0x++l3WDRC5t0ntis9mRd6IcqduzkH64DEYj/X0CYgcEo9uwcARMiIRkYlRt3nJ1bbECEjtVFcX445d0WORaeBjzMfPBQfBKiGn0PDKZFP1uGo++N9baGk397SP6TY6CxlOF9d8cR37wIJhKj8LhHo9y765UXwBRvfwx5uo4mGusSNlTiJQ9RSj/O0dn7DXx8A93P+txZpgLRuh0BPr27YuCggKRZ0LhYo1BuS1HjtTONOzq1XA1mMn74Jqj0hSUK0JhVZRX4hQnLSkGQD+mlHNDxvnXX39d7wfRNXGf8nEaIz4+XiT5NwyBay7kZVm7dq0QA2fD3r17RdtJPDi9LT/++ONZHYuunXJ/aKE8JQphoxA/yg86G0g00TFIMDk5ebJ2fgKGYRjm9H+X3tzzJvqW9BUiJwJ5mGlbhpXlQ1FQrIfKXY3Q8eGwee2Bp1ttJIjRqkS+7QrMGnkP/DRaWPJ1kHooMSA6BAM7zwBAC2A3WGDYWYDyXQWwFhrqn1gqEaFlNP/MDVOex6ZjU5Gd/hj81XnI0vdDSMglmDJ8OtRKFUzpFSJxnxL4yYPje3k3MR+NE6PegpLMClRlFqIqtxS6Ih10FSYUVathkdR/UFhTAxzaVCAWd5kBEWGAp68K7r5ucA9wh7m8CmuWVQmR42kuwKwnR8Mjuv7Dx8Y4k8BxJX5oCNQeCqz8vyMo86vNg5IrpRh+aSwShoeKY2k8lRgwLVospXk6WM12BEVxhAJzbmChcx5CVbrIK0IhYJTUTtW28vLyhEeEhAiFnVFuyauvviqKBtC+lOhPwscZCkaQSCJPAgkWqqTWVBEDKhDw66+/igIE9ENEVb2cHoXmQGFvFPpFXgwSSU4vDnkfKGmePEZ33323OObw4cNFTCWJKwq/uvbaa0UuDIkMqjxGuTskPJwV25rDww8/LMLFqIAAHYtEHhUjoHA2f/9/ZlI+HeTFIo8YeWCoD6htVDihpVB/k+CikDXKu6H3xcXFQsidLTQ2JBLJi0PV8Og+IOHEMAzDnJ4N2RtQnlaOaHM0VDBitu1P/FU2DIUlBvjGm+AzKA8eqn11c9IUWidjdN87MDUoEsYT5ShclgRrUW3IF3lpKFdGGaKFvcYKw+ESMYGmQC4VwoaKAVCYGM1LY6swoeKPdBjTKjFiziCYYtegwqDHZO9/piOgogMlXxwV5ZcpRM3vyjg4JBIUZlQiY1MyTh4sRKnejVw4LldF0QtqEbqmMFchoOQQQuQFCAqUIj/Xilx5Z5T494IOGhynyHMRfU7RHn9XIJVr4WUtwuznJkITeua/jWdDVE9/zLy7D1Z+cgQefm7CW0MV1BrDL5S9OMy5hYXOeQgJDSq1TE/wKfmdDGUqh0xVuSifg6AQJhIjVJyA8kio2ti8efNERS4nJC5IRJBHhfJtqGjD6aDSynSMoUOHCmFA5YwpT6W5UClrEjf0eVeoTDJ5ip599lnhhaIcGErEpxA88lxREQKCKrv98ssvQgyR2KBCAVSCmdrUHEgMksii49FnKcSMcn6oAlpzoNwa6gMqfkCiifqa2kp92hJIuFFRASoAQf1HnhgScFOmTMHZQuGA1C9UFIIKO1ChChr7huWxGYZhmFqsdive3fUuupfXehVG2Hfir9KhKCytQUBvA8IG13rFq8yeqJRejEn9b0GIbyDMuTqUfHoYprTaHB2JUgqH1Q5HjRXmjEqxOFEEa6AdGAJNYgCkfyfQk2ixGSyoOVyCyuUZonJa4ds6+F7RDSFRXqKoQE1SOWoOFqEmqUx4cqiYgN/VCTi+IQ3bfk2Hye6MzNAIQeNWUwI3SwXUcgs0agm03ioERHoifGAXaOInQPp3KDoFR1sKC1G16wDSdqSiuMSBGqsSNQ431EjdhScnQFqM6S9Phdrv3HpQgjt7Yd4LQ1vkDWKYc0GHq7rGMEzrwt8xhmHaG78k/4Jf//wVsVWx8HGUwTc7CUV6JQL76hE6oLbITrphKuaMeRE+WndYK0yoWpUJw/4ikU8CuQTaAQGQKbMhDw2GzCcc1mITLAX62lyaxABIZNXQb9oE/bZtkGrdoR02DNphQyH/O3qCRFPZ90m1hQBIsHTzhSmjstbr8zeqrj7wuaIbtn6zH0f21kZCyKw18K1MRqi/FdHDYxA4YRjkAQH/WjTYbXael4bpMFywVdcYhmEYhrlwqbHW4POdn6NvVV/xPqgsBbl6JYL66RDSP1usS9HPwoKpr0JqsaNyRSaqt+TWhaKpe/nBYTyE4pcegO3vSaclKhXc4uOh7t0LEqUKZZ9tgunECTggQZlPHMwqL2D7T5C89hMUYaHQxsei2/ypCLwjERW/pwkBZSQPDgkZbxXUvQOg6R0Am6cSf7y6BXl5tc+cO5dvw5D5A+E58uZmT1fRXHjyTeZChIUO0y6g0C/XOXBcoXA1ZwgcwzAMc2HzzbFvEJYXBimkiLCdRG6RFIGJJQjpXyy2n9BfghsnvQDjrgJUrckSuTWEMtoTCt8ilH16D8x/F3yRh4SI+fnslZWoOXBALIRNKkdB6HDkdJkCvayRed5ygX2Pb8HQERrEzZ8CtzhfmHOroU7wg7KTp6icVl6gxx+Pb0R1jQxSmxmJ5i0Y8NG9kLtMR8AwzL+DhQ7TLqDJMynPqDGaKrLAMAzDXDjsLtiNJTuXoH9Nf0hgg6IgF77dDAgdVCtyjusuw42jnkDZJ0dgya4W6+T+aqiijKj86UXUHKwtL20KiUHB6FuQU+kBtYcSXl6Ah60c6tJM6GukSHd0gdFcWyRApZEjKNqLyrzBZjLBVl6J8mIjjCpfrNsFJO/+EmMfmgzv3p3FnDdZR0pwbHUyMlMMsEMGlbEMQ/yOI/7FJyBVKtuw9xim48FCh2kXNJx/h2EYhmFciw98dPAjfLP3GwwrGibWdTMnocAoQ9yg2smXj1VfghsHPYKyjw/DVmYUldTcoi2o+ut1lH9a66kx+EQif+RNyKryhiNfHBkmgxUVhfSayjl3qzunu48KvcdFiLLJSrf65pRZZ8SGl/5ESrE3ctAJ3z+7GxH+RuRWaGASldMIGbzLkzFyCBB+52OcuM8w5wAWOgzDMAzDtFvydHl4aP1DMKeaMaFqAmQOGTQwoDq3CkF9y6BQ21BoCMaVXe9F6ceHRQU1qbsEpmP/Q9X3W8QxdL6dkTt4PnJrfIG/C6t16u6HxAkRgB0oy9ejrECP8rzaCS4TRoQidkCQmFSzMZTubpj43BzErT+E9d8kQafwR3pFbXU0uUWH4LKDiA6qQdSN4+A5ftx/1VUMc8HBQodhGIZhmHaHxWbB0rSl+H7t9+hS0gVqW62nJEpejMiSvTii9ERUj9piAv62W1DzTYqomCYPVKJy8f2wVxajyr8rcgdei3yDN/D3vJ+dEwPQb0okAiP/qeQUkXB2IdKdxvTCVYO6YsfrS1Ger0enzm7oMr4HtD2nQSKTtUY3MAzTBCx0GIZhGIZpN1SZq/Bj0o9YvWs1QopC0MPcQ6z3VEswxbIanYzH8XnRQISOy4NU5kBpRU8M3RVFs9zALd4bVb8+hUqbGzKGP4ISeZgQOFS5OaZ/EPpNjoRfWOtOYinXuGH443Nb9ZgMwzQPFjoMwzAMw5z36C16vL//fWzbtw3RpdGIt8SL9RI5MEabiaGVSyGHDWuNQ6AIMsI7WgebXYrEtKvEftrBITDs/ByFBSYc7HM3bDKVqH7WbRAJnCh4B7VuOWeGYdoeFjoMwzAMw5zX2B12PLj8QSgPK5FoSRTrZAophoTYMTTnU2gqq2GVe2CrZi4OnkhB19m1k4KaSsZBUxoMuZ8bYDmE7LX7cbDX7ULkhMf5YMzVcfD0dxYHYBimo9F4Fh3DNEJUVBTeeuutuvc0S/Pvv/9+zvpqw4YN4hwVFRVn9fkvvvgC3t6NzG9wnvBvr49hGOZC4bPDn8FxzAFPiyfkSjnGxGhxn/xbjM96Exp7NfL9x+Ob0inYsTMZPl3LoPYzocaiRs/js8Tn1YkqJL/1BQ70Wgib3A1h3Xww7bZeLHIYpoPDQqcDM3r0aNx1113n7Pj5+fliIs8LidYUd0OHDhV96EUTNLQDYcYwDNNWc+Ms3r4YAcYASCQO3O62FKNSn4O6pgAW7xhs9FmI77dYUJqXD+9oKQKGVInPeRTMgdykhTLaA2nvvoED8TfBJlcjNNYL0xb2glzJxQAYpqPDoWsXOA6a4Mxmg1ze8lshODj4nLTpQsBisUCpVHIfMgzDNEFJTQke3Pggupd1F+8HOA7Au+oEHNpgnAi5Gpt3ZKCq+BCkChsiJ8vgGXIMUokDFfpQDEwaJR7nVmZvxe6AS2CVaxAcpcW0hb2hYJHDMBcE0o5ouFtMtjZZ6Nwt8bYsWrQIDzzwAHx9fYXB+9RTT9Vtp3CmBQsWICAgAJ6enhg7diwO/j1jM3Hddddh1qxal7wT8t7QcZ3bN27ciLffflt4IWjJzMysC5davnw5+vXrB5VKhS1btiAtLQ0zZ85EUFAQ3N3dMWDAAKxZs6bZ3g1qu/M8rgt5KQi73Y4XX3wR0dHRUKvV6N27N37++ed6x1u2bBm6du0qto8ZM0a0tyXQuTp16gSNRoOLL74YpaWlp+yzZMkS9O3bF25ubujcuTOefvppWK1Wse2ZZ55BaGhovc9NmzZNtIXaT6F7BB2brs35/kzHdfbVhx9+iBkzZkCr1eL555+vF7pGr+fPn4/Kysq6vqM+pTb16FFbUciVxMREPP744y3qH4ZhmPaEzW7DQ5sfgqxMBj+THxSwYAR2IafLAnxXNgV//bETVcVFCIgHYq8uhHfocSFyjpQOR5+UpyFxyOEWp8HONCWsCi0CAqSYfle/Uyb3ZBim49Lhvu1Wsx3/d+fGNjn3TW+PgkLVfFf4l19+iXvuuQc7d+7E9u3bhTgZNmwYJkyYgEsvvVQY/CRIKLTp448/xrhx45CcnCyE0ZkggUP7kpFMxjJBoskpHh566CG89tprwij38fFBdnY2pk6dKgxwEj9fffUVpk+fjhMnTgjxcCbuu+8+3HLLLXXvv/32WzzxxBPo37+/eE8i55tvvsFHH32E2NhYbNq0CVdffbVo06hRo8T5Z8+ejYULF+Kmm27Cnj17cO+99za7L6kPb7jhBnEeEoArVqzAk08+WW+fzZs3Y968eXjnnXcwYsQIIe7oXATt++ijj4rPkcD87bff8P7772Pbtm1CYEqlUuzevRuBgYH4/PPPMXnyZMj+ngPhTMd1QsLlpZdeEnlO5EFLT0+vF8ZG66nPqM8JEpwkgkg00blJfBL79+/HoUOH8Ouvvza7fxiGYdobHx/6GDvzdmJ8xXjxvq/9ENaVJSL1eO1vpDZQgtCxVmi9ksX7YoMfqlT34NrQEdDtTYdUI0f6lpWo9BoMGayYes9IFjkMc4HR4YROe6JXr151hjAZ/++99x7Wrl0rBM6uXbtQVFQkRAdBooS8J+QFcRrRTUHiiEKjyLvRWIgZiR8SVE5IPJGXxcmzzz4rjP2lS5fi9ttvP+P5yCinhdixYwcee+wxIeRIaJlMJrzwwgvCQzRkyBCxDwks8iSRgCOhQ96OLl264PXXXxfbu3XrhsOHD+Pll19uRk/WCjsSH+QhI8gzRCKFhIsTEgwk8K699tq6NtB10mdoHEi4kBgjbwntR8Ll008/rRN6JMoIyqNx7dMzHdfJlVdeKbw2TlyFDo0VjRl5clyPTX06adIkIa6cQodeU5/ReRiGYToiB4oO4KODHyHMEAYvkxeUMMFYUI70Sm8otDZETZJD7Z8EqcQOu0OCfSWTMHXIY+hcKkPF0jRxDEWkGUdSOwMaoNfQALj7uLX1ZTEM8x/T4YSOXCkVnpW2OndLhY4rISEhQtyQB0Gn08HPz6/e9pqaGuEtaA2cnhYndD7yOPz1118iQZ7Cruh8WVm1JTqbC+1PHhXy8MydWztBWmpqKgwGQz1hRZjNZvTp00e8Pn78OAYNGlRvu1MUNQf6PIWUNfy8q9Chft26davwWjmh/CSj0SjaR6KQxAOJyptvvhmXXXaZECdnojnHbazPm8uNN96I66+/Hm+88YbwLH333Xd48803z+pYDMMw5zsmmwmPb31chIMP0tf+XehhOYpUnSdCBhbBr3cV5FKLWH+ktAc8/RfitrhE6H/ORnmRQaxXhGpxaNnvqPEZDZXMgv5ze7bpNTEM0zZ0OKFDT8RbEj7WligUilPaTrkgJDpI9FDeRkOcVbnI4G2YE0QJ7s2F8kRcIWGyevVqYeTHxMQIr9KcOXOEGGkuer1e5KCQwHCGyxF0PQSJqLCwsHqfcXqs/guoHeR9oRC5hlBujRMKqyPvDoX5keA7U6GG5h63YZ83FwohpH4iDxt5fmicaWwYhmE6IuTJyazKRII5ARK9BG4OIyry9AgdUomA7uVin9SKaOiVN+Ka2DGwb8pBZV5tOJtELYfHqHDo0rcg1b3WCz5oVgyHrDHMBUqHEzodAUpqLygoEAa2a8K7KxRGdeTIkXrrDhw4UE88kVFMnoXmQB4JyhFyekXIeG9JMQASXZRzQ0Lt66+/FqLNSUJCgjDUydtDIVeNER8fL8LkXKEQuOZCn6c8naY+T/1K+S8k5E7H4sWLRe4LiUzySFEIGokYJ9S/Dfu0OcdtDqcbL7oPKCyOQtZon8svv1wIUYZhmI7GsdJj+PzI55A4JOhT3QdGGNHVdBz5Kgm6xdeKnG0lt+PqLpdDu6sIpi1/CxylDO4jwuAxPAwOsx4b30mFNXAYvLQWdB/HYb4Mc6HCQuc8ZPz48cIrQiFgr7zyisg3ycvLEx4REiIUAkVV2F599VVRNID2pdwSEj7OUDCCRBIZ/yRYKNejqSIGlCNEBj55D0ikUEUvEi3NhcLeKAdn1apVQiQ5vTiUd+Lh4SE8Rnfffbc45vDhw0V1MRJXVFGOjHgqZED5Offff78oBrB37966im3NgSrYUSEH8khR9biVK1fWC1sjKNH/oosuEjk35BEhrxiFnVG/Pffcc8jJycGtt94q8oKojSQsaH+aK2jw4MF1fUp5VHQuEm9UyOFMx20udGzqNzo+5UtRyJsz7I36hMQcQf3GMAzT0bDYLXhy25OwOWyYppwGY7URWocBRblGhI4vgkQKnCzth5sqxsHyUyoo3kCikEI7JFR4cWTa2gd9aa99jmz/2pC3Edf1gVT6z4M3hmEuLFqUVEIVrSghmgxXqj5FhrizQlRT/PTTT4iLixNhPD179hRlhJnTQ0KD+mjkyJEieZ2EDj3FP3nypCj/TFCCOokRSninMamurhaVv1whcUEhWORRIQ9QU/k2lP9BRjtV/yKxQ8cnT0VzoVLWZKTT5ynszrmQh4Qgzwi1l+4hMtipcAAJNyo3TZBI+OWXX0TBBTLyqTobFTBoLiREPvnkE1GUgD5PgosKIrhC1/Tnn3+KbdRn9BnKdYmMjBQeKfJoDRw4sK74Au1Pwoc8VU7hRmKMQvwiIiLqRGVTx20J1Hck+Cg3iMaLRK6rEKXt9D1qmMvEMAzTESBPTlJZEvzkfvDJqQ3TjtQnQRZshGeEHja7HKOPXQZLeiUgk0A7JATB9w+A99RoIXLod7xs8Y/Ye9ABh1SOsBAJInsGtvVlMQzThkgcLZj8hYxTMrjJmKPchUceeUQ8tT527Nhp8w+o8hUZ7GTg0lNvSqSmJ+b79u1rdH6QxqiqqhKeAfICkAfAFUr4zsjIEAazaz4Ew3Qk6GtKYue2224TJcn/S/g7xjDMuSatIg2X/nGp8Orc7nE78g/lw9tRAWlqMrrMzoCbjxnazKkIT54Lde8AeE2OgtyliprdYEDaky9jb6YfSv3ItnDg8icGwS+0thoowzAdi6a0wVkLnYYUFxcLzw49zScx0xj0dJqS1OmJtxN64k0lfOmpfXNgocNcyND37IcffsDDDz8s5hsiz9t/CQsdhmHOJcWGYty0+iakVqRiVMAohOwPhsViRbfyPTD7lyF8WCHMJg8kbHkFSm8fBN/TDxLZPwEpNSdSsOWp75DqPgh2mVJMGjrkklgkjj/zHHAMw3RsofOvcnTo4ERTuR80EWbDJ9AU6kMhSqeD5l2hxfVimAsbypOhiTkbgzyLtHRU6GGCv78//u///u8/FzkMwzDnkpNVJ3Hz6puRq8uFv9ofI00jccxyDEH2QhSW2xA/qVjsF5J+CWQ2NbwmRtaJHFtlJVK/+gvbdzug9xoh1gUHyzD2lv7wCT67KpcMw3QszlroUFL5XXfdJZKymwpBo+phzrwSJ/Se1p8OCnNzrXTFMDRxJ83r0xhNCe2OwL9wujIMw7Q5hfpCKGQK+Lr5nlJh7dY1t6LMWIYIjwi83O9l/PrFr2KbtjgLPv2KIXezo6Y6DH7ZI6EIc4dbD38Ydu9GyY8/41CSHCdDxwAaKRQOI4bNiUXC+Jh6VT8ZhrmwOWuhs3DhQpGfQ7PbtzYUouPqBSKPDiV/MxcuDeffYRiGYc7/hzQ/nPgBr+x6RVRSS/BLwLCwYRgRNgIGqwH3bLgHeose8b7x+GD8B1izZLX4TLg1G1V2M+L+njOnS/JVkEAKdQ85MqZPR2mRGcfi5kH/99+FqCAjxtwzFhovztNlGKYVhA5VpaKcG5pYMTw8vMl9g4ODUVhYWG8dvaf1p4PK9v6XE0kyDMMwDNN6GK1GPLvjWSxN+2d+tKOlR8Xyf4f+r27dwOCBeHvM2ygrKENS0glIHHbYC4oROqy2nHRNcW94lCZA1cULJR88hxO2rsjsOxUOqQxuaglGz+uOLn24shrDMK1QXpqetJDIoRna161bV1cauClojheaF8QVKs9L6xmGYRiG6VhQvs285fOEyJFJZLiv/31Yd+k6PDvsWUyKmgQPpYfYb0LkBOHJMVWbsHRJbd5uJ3MG4FUFr0gdbHYp4k9cIdbLPHJxvDoCGdHThcjpnBiAK54exiKHYZjW8+hQuBqVh16yZImYS8eZZ0NVD5wztdNcLhRmRHk2xJ133olRo0aJ+UemTZsmqkft2bNHJFYzDMMwDNNx2Ji9EY9tfQwVpgr4qHzw2qjXMDBkoNg2K2aWWKx2K/J0eSIvJyUlRUxWTdUd3R066PMrEHVRkdjfkTseKkMw3Hr4IvfzN5DV6WaxfuTlXdFjVBjn4jAM07pC58MPPxT/jh49ut56mkGeJlskaFJKmhneCU1ySOKIJm+kylg0FwhVXGvuHDoMwzAMw5zfpFem47Xdr2Fzbm11zO5+3fHm6Dchr5Hjiy++EJNXd+vWTSz0cDTcPVyEv69fv17sH448RJUeQE6UAmpfE4xmDXqkzASkEjh0+3FCXVs6OqSzB4schmHOjdBpTvWnDRs2nLLu0ksvFQvDMAzDMB2HSlMlPjr4EX5I+gFWhxVyiRxXxV+FO/regRNHT+CPP/6AxWIR+6alpWHZsmUICQkRebiZmZlifX8cxDDLDnyt64tuU1LEOp+TsyGzaqHp54u0999AYdztYv3wy7qxJ4dhmHOTo8Nc2ERFReGtt96qe08lPJuaD+nfQqKZzlFRUYELBfKMzpo1q62bwTAM0yQmmwlfHf0KF/12Eb45/o0QOaPDR+O3mb9hUe9FWPHnChGSRiInSmPAeP8iRHjXPlvNz88XIkcGO2ZgFSZKdmCtYyYCehdBrrZBpwtGROZoSLUKmJL/QnLwJPG5rgMCERh5+okBGYZhWnXCUOb8hkIMExMT64mT1oT+WLWHCSxJMI0ZMwbl5eXw9vbG+QD9kadiHvv37xdjxDAM0x6w2C1YkrpEeHEKDbUVVWO8YnBX77vQTd0N5Xnl+HT9pygqqs2zGaU8glGGNZAaHBgOQAcNkuUJyEEw+ll3QyWR4duSSdAZ0xE35u9y0ilXQuKQQzvAAwdfPICK+BsgkwGDL45p02tnGKb9wULnAofCEW02G+Tylt8KTZUIb4+YzWYolcq2bgbDMMx5yZqTa/Dm3jeRVZ0FOIBell7oaegJe64d6w+sB/3nROumwCWW3xBak4E1VX0h8Y9FZ00JOtXsQl/rHvQFcNzWHasyg+GQFKDz1CJIZQ7UlPSAd3EvKMLdUbniM6RGXiSOlzghEh6+PE8OwzAXeOgaGe4Wo7FNlpbMYE/elkWLFuGBBx6Ar6+vEA1PPfVU3XYK11qwYAECAgLg6emJsWPH4uDBg02GON111111hSJo+8aNG/H222+L8C9ayIvgDAdbvnw5+vXrJ+KkadJXip2eOXMmgoKC4O7ujgEDBmDNmjVNXoNr6Bq13Xke14WSUAm73S4q8ZEXgyr09e7dGz///HO941HsdteuXcV28sA447ebw8mTJzF9+nThYdJqtejevbs4Hh2DjkXQNmqTs3AG9RWVS6d+8/f3x6RJteERNBHulClTRD9Qf1xzzTUoKSlp9tgRSUlJGD58ONzc3JCQkCD60rW/nKXZ+/TpI9Y3LPDx2muviTh2Pz8/Ue3QGePOMAzTFmzI3oC7N9wtRE4EInCV7irE5sbCWG4UD4kIjUYjfrcSQ1W4xfg+fGqy8V3uEBwu0ODQkVz8vsuED04MwG/SBVgmvRrLkn2h9K5E3GU50AZWw2JTIP7ElYAEcIsxIelwDWo0QXDTSNF3ciQPPMMwLabDeXSsJhPeuXZOm5x70Zc/Q+HW/CdOX375Je655x7s3LkT27dvFwb4sGHDMGHCBFG8gQx+EiRUoebjjz/GuHHjkJycLIzrM0ECh/al6nbPPPOMWEeiySkeHnroIWFMd+7cWQiA7OxsTJ06Fc8//7wQP1999ZUQDidOnECnTp3OeL777rsPt9xyS937b7/9Fk888QT69+8v3pPI+eabb/DRRx+JyntUbefqq68WbaLy43T+2bNnC6P+pptuEiXI77333mb3JX2O/tjScUnoHDt2TAiViIgI/PLLL7jkkkvEtZBodJZCd47Brbfeiq1bt9YJTBKVJDLffPNN1NTU4MEHH8TcuXPF3FHNGTvykJEIpX6j7dXV1adcy65duzBw4EAhgEiUuXqSqAoRGQv0b2pqKi677DIR3nbjjTc2uz8YhmFai3JjOZ7a9hTkdjlmOGZAmi2F0WGEQqEQv99xkSHwrE6GsmAvkPkbkLUdOQZPLMnvD6vMjM5TyqH19kfJcaDoeA3Sj56gx5Lw716O0KHFkErtKNQHIPr4Iqj0odD08Ufe2w8iI2qBOP/gi2OhdOtw5grDMP8B/MvRhvTq1QtPPvmkeE3G/3vvvScmVyVDnAxhinEm0UGQKCFvAHlBSAicCRJHZDzTE7bGQsxI/JBR7oTEE3lZnDz77LNiYtilS5cKr8eZIFFBC7Fjxw5RTpzEAAktk8mEF154QRj1zoliSWCRJ4kEHP2hpNLlXbp0EfMtEVSC9PDhw3j55Zeb0ZO1Zc1JzPTs2bPu+K7XRgQGBp6So0P9/sorr9S9f+6554SXhdrr5H//+58QTCQcyePU1NhRn9KEuOQhI++Zs+9JQLr2Nwk8gjw2DceHhCcdj8qxxsXFifmn6NgsdBiG+a+hSIXndjwHvU6PKQVTILFI4IADCXHdMCm4DF4H7wXWJNX7zNGKIKwq7AaVjwHdpuZDoTECKEHQICBwoASwRMCit0HpU5vjs6ewN+KN9yCizAGJmxyWrNU4Ie8Dq8IdPkFqxA8N4YFnGOas6HBCR65SCc9KW527JZCx7Ao9xSdxQyFqOp1OGMGukHeBDOjWwOlpcULno/Crv/76SxQZsFqt4nwkIFoC7U/eDPLwkBeEIK+EwWCoZ+gT5IEhUUEcP34cgwYNqrfdKYqaA4WSkWdm1apVGD9+vBA9Dfu3MSh8zxXqe/KkOEWbK9T3rkKnsbEjyHNEwshVwJD3prmQh4dEjuuxSfQxDMP81yzPWI5VmaswqngUlBYlvL29cFFnO2JOPAEk1f7mEZWarsiSxiGjUoOU/Fx4RVei09gCyOQ2FOgDsb+oJ3r4H0eERx6gzAI5sa12Kf5Kn4356qsRkFxbXVPTU4mUl/9Ebp8HxPuRV8ZBKutwUfYMw/xHdDihQ/kOLQkfa0vI7d+w7ZTLQqKDjNvG5iRyeiRoUtaGOUEtyeOg8C5XSJiQJ4I8RzExMcKrNGfOnLrY6+ag1+sxY8YMIVCc4XIEXQ9BIiosLKzeZ5weq38LhZpRjg2dg8QOhcqRd+iOO+5oUT9QWylkrzFPEo3JmcauNTiXx2YYhmkuRYYiPL/zecRXxMPf6A+lTIJrjF/Ab19tCHShvAsOYAiy8vWoEnmMVDWtDEF9SxAyoDav8WhJHDoZH8HtXSKwwWLE12lJkJi3IcozCxXV43CfKRHS9FqRox0YhPJvHkFy5zmARIoufQMR3u38r+zJMMz5S4cTOh2Bvn37oqCgQFRCo7lrGoNCnyhp3pUDBw7UM5IpdI3yRZoD5ahQnsnFF19cZ/C3pBgAiS7KuSGD/Ouvv643oRsl45OgIW8Phak1Rnx8vAiTc4VC4FoCeVEoT4iWhx9+GJ988okQOs78l+b0BfU95fRQv59NJTpn2B3lHBUWFopiBsTu3bvr7dOSNjEMw/zX0G/6E9uegLJSKYQOcZFtGfxsmShVxWJbTX8kH86hUjCQuVnh3dmEgDgllAFVULjVipyt2WMwp+ImeOabgGMnMV4CTAmLgD36RuQZLQhKKQXMBkjUcvheEgvDziU4WeKGyoQYyBUSDJvD5aQZhvl3sD/4PIRCr8grQiFg5J0gwbFt2zY8+uijIkmfoIR5ek1FA1JSUkS+SEPhQ8Y6JcPT56lqWFNeAcozocndSCxR+NaVV17ZIi8Chb1RDg7l3JBIIqFGC4W/eXh4CI/R3XffLfJ2KARs3759ePfdd8V7gsQJXcf9998vQr++++67uoptzYEqp61cuRIZGRni2BR+RuKJiIyMFMLrzz//RHFxcZ2H6XRFDcrKynDFFVcIcUJtpePOnz+/2aKEQvQo3+jaa6/FoUOHhIiknCXCKQApX4i8ZitWrBCCqLKystnXyjAMc675JeUX7M7ajYHFAyGBBImSJHQyZ2K59WJ8eTAUycnZ8IuvQPdr8tHz2hRETciCNiIVCrciWO0ybEubh+tyFgiRI1FIoQjVipLUlhwdbJvzELS7GDDboYz2RNCdfSHz0CP/vY+R2mW2OH+/KdFcTpphmH8NC53zEDKGqTTyyJEjhYFNeSGXX365KKHs9BBQmNbjjz8uShxTKWiq7DVv3rx6xyFxQbke5FEhD1BT+TZvvPGGSIIfOnSoCN2i45N3o7lQKWsSEPR5CvFyLosXL64rbkDtpZAyEiCTJ08WYWbOMstUoYw8KVRwgYoiUHU214IAZ4JECIkU57Gpzz744AOxjcLlnn76aVFpjvqvqeIKoaGhQpjQ8SZOnCiKG5CIopBBChdsDtTndB3UHzQ2FFZHIpWgctMEeYveeecdIQzpnFTam2EY5nwgpzoHr+56Ff2L+0NtU8NPpsfAmq34KnMAjqUUwzOqAglX5yJiJBUaqA07y9UFY0P2MPyQfAOK097D/MxxkFZbIPdzQ+DCRAQt6ouQRwbC55JYuHX3gzxADc+JkQi4sReseWnIuf12ZISMhVnlDU9/NyROiGjrbmAYpgMgcbRk8pc2oqqqSlQRo6feVB7YFaPRKJ7ik8HsNCIZ5nyDxBPNq0OFGcjb057g7xjDXDjYHXZcv/J6VCZXondZb8gkwHzbt1iX1QkGbzsiRlbCzbvWA11t1mJN9hRovaZiuG8YepklUGfpYM6sEtvdEvzgNTUMpR+9B4lKCa9p06CKj6/zbNtNJpS89z5K//c/GJR+2DngUTikcky9rReie/m3aT8wDNN+tYErnKPDMOcAKs1NldsoJJDEzZ133inm2WlvIodhmAuLr499jaScJEwor62SOdGxHuklGtT42hEzLQukUYxWFdZmj0Wc5zw85uEJe0ol7NX5Yn9RvkYCeE2OgjLagazrroI5tbZaaNln/4OySxd4Tb8Iqq5dUfTKqzBnZsIi1+DE4DvgcMgR2cMPUT3rVxxlGIY5W1joMO2CKVOmYPPmzY1ue+SRR8RyPkGhhDTRKIUL+vv7i7wr5xxBDMMw5yNpFWl4Z+87GFQyCDKHDJ1lhQitTscv+u6Im5IuRM7ewt6Q2RfhHqkfsLcCVhSLz1IejqqLN9xiveEW5wtT6mGcvHQRbJWVkAcEQJ2YCN3GjTCnpaH4rbfrzmkK64rDfRahSkcVU2UYfmlsvWI2DMMw/wYWOky74NNPPxWFDRrDOSHo+QTlSzXMmWIYhjlfsdgteGTLIwipCkGgMRByiR0TzcvxR0ECOo3Jh0JrQ7EuGFOqHoBPpgVwVAjPjbp3ALT9g6CK8oJEXpvHWL74RxQ8+yxgtULesw/yL3oQCk93dLn9USgOb0HVX3/CeOQoTGPmYK91EIw6G9x9VbhoYW94B2nauisYhulAsNBh2gUN599hGIZhWo9PD3+KtMI0TCqbJN6PdWzGgaIAKGOq4RWpg9UmR+KRhXCvqp2vTd3TH+7DAmE8vA26NRtRUVQEa3ERLHn5MB49KvaRT7kEe30uQun2UgCl2LcS8A0NR+zcJ+E2X4GtPybDbrUhMNJD5OVovVpnXjWGYRgnLHQYhmEY5gJmX+E+/N/B/0O/sn5Q2BUIkZbCrzIL+6XR6Dq4dj41TeplcK+KgDLSE94zu8CUvAfZ198OS15eo8eU3fQgthTGwJBngNpDgeDOXjh5tBRleXrsXJJet1/nPgEYPz8BCqXsP7tehmEuHFjoMAzDMMwFypqTa/DQ5ocQoAtAuD4cEjgw2bISy0tiEDUjC1K5A1XFvdD/5HhI3RXwmuyPopceRfWqVeLz8pAQaAcPhjwwEPLAAPFvni0E6/8qgdVihm+oFtMW9oKnnxomgwXpB4qRvKsQeakVSBwXgcEzu0Ai5ZwchmHODSx0GIZhGOYCg2aWoAprr+15DTK7DIMqBon1Q7EHKcUe8OlfAjcfM/RGT/Q5skBMGqoIyEfmnNth1+tpwjD4XnstAm5fCIfCDaV5OuRlViE/vRLJOwvFsTp198OkBd2hVNeaGiqNAvFDQ8VC5+eiAwzDnGtY6DAMwzDMBYTNbsPLu1/G90nfQ2qXYpZuFhxmB3wk1YgzHMYyTRfEdK+dYDrs2M2QWzwh99Oh5M3a6pbq3r0R9NSTyDX4YfO7x1CcXQ27tf6UfD1Hh2P4pTGQyhqfaJlFDsMw/wUsdBiGYRjmAsFqt+KeDfdgffZ6IXKuMF4BY6kRCqkdF9v+xMbyboiYmi321WePQbeS7pC6S1D+9aNinf9tt0E+dz7W/JiKrGNH6o6r0soRFOmJgEgPhHXzQUTc+VcNk2GYC4/GH7UwTCNERUXhrbfeqvdE7vfffz9nfbVhwwZxjoqKig7TZ61JR+gfhmH+W35I+kGIHDe44TrLdTAWGqGQSXG1/WeUlEsh714OlacFhhofJCbPFVaCYePbgLkG2mkzkB41DT88txtZx8oglUvQf2oUrn52CG54bQSmL0oUOTcschiGOV9godOBGT16NO66665zdvz8/HwxkScDfPHFF/D29j6lK3bv3o2bbrrpX4vDxsZy6NChYgy8vLx4CBiGOSMlNSV4/8D7wpMzt2YuKvMqoZCTyPkJfpZi7JOHwb9Hudg38vj1kNrUsBZuhSX7CCTd+2GTZib2LMsUYWqdEnxxxeODMGhGZ3gFqDkUjWGY8xIOXbvAoYRQm80Gubzlt0JwcPA5aVNHIiAg4JwdW6lU8hgwDNNs3tz7JvRmPSaWT4S+Sl8rcmw/IdKRhT8s4xAy7DgkEsCUOwy+JT0Bexlqtn0NqZ8/jg28HZUZBmi9lBg+tyu69A1gccMwzHmPtCMa7nazrU0WOndLntAvWrQIDzzwAHx9fYXB+tRTT9Vtp3CkBQsWCEPZ09MTY8eOxcGDB+u2X3fddZg1a1a9Y9ITfzquc/vGjRvx9ttviz9GtGRmZtaFOy1fvhz9+vWDSqXCli1bkJaWhpkzZyIoKAju7u4YMGAA1qxZ0+Q1uHonqO3O87gu5Okg7HY7XnzxRURHR0OtVqN37974+eef6x1v2bJl6Nq1q9g+ZswY0d6WsHXrVnH9Go0GPj4+mDRpEsrLa59Omkwm0d+BgYFwc3PD8OHDhbfFibNf1q5di/79+4tjkMfkxIkTdftQ/1O7PDw8xJhQ/+3Zs0d8dv78+aisrKy7budYuoau0Wvi4osvFvs43//bsXQNXfvll1/QvXt3Ma50/Ndff73ecWndCy+8gOuvv15cR6dOnfB///d/LepnhmHa51w5S9OWoktVF7hXudcTOSk+01EdlCOqrJmMHuh+4kpAYod+3VuAXIbieS8hL8MAuUqGGXf1QUy/QBY5DMO0CzqcR8dhsSPviW1tcu7QZ4ZC0oJJz7788kvcc8892LlzJ7Zv3y4M2mHDhmHChAm49NJLhcFPgoRCkz7++GOMGzcOycnJQhidCTKKad8ePXrgmWeeEetINDnFw0MPPYTXXnsNnTt3FqIgOzsbU6dOxfPPPy+M5K+++grTp08Xhj4Zw2fivvvuwy233FL3/ttvv8UTTzwhRANBIuebb77BRx99hNjYWGzatAlXX321aNOoUaPE+WfPno2FCxeKUC8SEPfee2+z+/LAgQOif8iAp2snD9X69euFt4ogQUkigPo8MjISr7zyihBCqamp9frz0UcfFeKA2kXXQ8cjAUVcddVV6NOnDz788EPIZDJxToVCIQQRiRm6XqcwIrHYEBJWJLQ+//xzTJ48WRyjOZxpLJ3s3bsXc+fOFSLrsssuw7Zt23DbbbfBz89P3FtO6PqeffZZPPLII0Js3nrrrWIMunXr1uz+ZhimfRUgeH7n81DZVOhd1Vusm2xdjUhkId1/JtalFqDLRaVifVjSdZBZtTAe+xV2XQHsi17Awf0msW3M1d3gG6Jt02thGIa5oIVOe6JXr1548sknxWsy/t977z3hUSCBs2vXLhQVFQnRQZAoIe8JGaauOR+ng8QRhTaRZ6KxEDMymElQOSFjn7wsTsgQ/u2337B06VLcfvvtZzwfGfZO437Hjh147LHHhKgg45y8KeRFIA/RkCFDxD4ksMiTRAKOjGwSD126dKnzQJDRffjwYbz88svN6EkI4UKi6oMPPqhbR54NQq/Xi+OTd8mZU/TJJ59g9erV+Oyzz3D//ffXfYaEHrXHKQanTZsGo9EovEBZWVli37i4uLoxc+1v8q40Fc7nDGOjXJ6WhP2daSydvPHGG0LsPf744+I9eceOHTuGV199tZ7QIUFLAoh48MEH8eabbwpRyEKHYTomi08sRnJ5MoZUDgGsQCgK0QeHkREwC6tOFCB6cgYkUsCSPwB+Rf3gMBXAkrwS0rEXYWuaH0kl9BgVhq4DOFyZYZj2RYcTOhKFVHhW2urcLRU6roSEhAhxQyFSOp1OPIl3paamRoSYtQZOT4sTOh95Av766y+R4G61WsX5yLhvCbQ/hWGRh4e8CwR5TQwGQz1hRZjNZuEhIY4fP45Bg2onrHPiFEXNgbwr5AVrDOozi8UivGVOyBMzcOBAcd7TjQmNB0FjQl4t8r5ROOHXX3+N8ePHi/ORODtfoGuh8ENX6JrJ20SeLacHyfUaneKMrpFhmI5ZgOC9/e/B1+iL0MpQsW4q1iHLayxWJBWi8+QMyN3sKCuPxqBj1wMSB/Sb3oND646DIRfDlGtEYKQHhs/558EOwzBMe6HjCR3KYWhB+FhbQsZ2w7ZTLguJDjKyKQejIc7KXlKp9JScIDLmm4tWWz/8gIQJeTjIcxQTEyO8SnPmzBFipLmQ52TGjBlCoDhDrAi6HoJEVFhYWL3POD1W/xZqb2uPiXNCOxoTgoTglVdeKa6DQgrJG/fDDz+InJt/w78dy9a67xiG6YAhazueh86sw7TKaWJdHxyB1WDBipIaRE9Jh1xtQ05FJIYeuF9UWTOlLIFDX4Tcy19Cca4RKo0ck27sAVkLH+QxDMOcD/Av13lI3759UVBQIPJMSHS4Lv7+/nVhUOR5aejVcIXCnZw5KmeC8lAovImM9p49e4qn/C0pBkCGOuXckMFMHg/XWa8TEhKEoCFvT8PriYiIEPvEx8eLcD1XKASuuZCXgsL+GoO8LtQXzlwbp5CgnBlqW0ugcLC7774bq1atEjlFlG/Tkr4mkdFwv9YaS+pD12sk6D21ubn5QAzDtB8K9YWoNFU2us1is+CBTQ9gTdYadNF1gZvBDSqJFQPMe7C8OgZRk9OgUNuQWxmOxMMPQm3RAPZSmI8uQ3XiZCQXeIjjjJ+fAE//1nmQxDAM81/DQuc8hMKiyCtCIWBkUJPgoMRySpSnJH2CqrDRayoakJKSIrwLR478M0u1s8IWFTqgz5eUlDT51J7yTX799VdhYFPoHHkuWvKUn7wdlINDOTfkwSGhRguFv1F1L/IYkUCgvB0KJdu3bx/effdd8Z6gxH+6DsqBoYT+7777rq5iW3N4+OGHhXCh3JNDhw4hKSlJ5OXQdZP3ihLu6dgrVqwQeSs33nijCKe74YYbmnV8ug7KVSIv28mTJ4WAoPORuHD2NV03iS06Jx27MWg/2of6xlkRrrXGkoo30LEpv4qKF1DfUt4X9T3DMB2LIyVHMPXXqZj480R8fuRzIWyc1FhrcMf6O7D65GpoHBr0r64NVR7r2Iy9VSGImnISCo0N+VVhSDz4EPxqNJBqAN2ql2FRanA0rDYEtufocET1rH24xjAM0x5hoXMeQt4QKrU8cuRIUbaYnshffvnlwsCm8s8EVQyjpHOqJkaloKurqzFv3rx6xyEDl57kk9eCvAZN5dtQIjtVX6MKYlRtjY5PnqXmQuWPydCnz1PYnXNZvHix2E7GN7WXqq+ROKCqYxQCRuWmCcqBoapoVHCBiiJQdTYqYNBcqI9IFJJIo9wbEopLliypmx/opZdewiWXXIJrrrlGXBflDa1cuVJcc3OgfiwtLRV9TOei/CMqbPD000+L7XTdJNao2hn1NRVHaAwqtkAhguTJcuYntdZY0nX9+OOPIpyOikBQFTgKIXQtRMAwTPuHhMzDmx+G2W6GwWrAG3vfwOyls7Etd5sIU7t1za3YmrsVapka82XzYTVZESitRFhNOnRdrFC6W1Gm80Ovgw/B1+gOmZ8Khu1vwmGswMkJD8Cgt8M7SIMhs8+fHESGYZizQeJoyeQvbURVVZWoPEXzlND8Ja5QRayMjAxhMFNlLIZhWhf+jjHM+cVLu17Ct8e/RYA6ADf3uhkfHPwAZcYysc3XzVe89pJ54SrLVSg8WSjWX+f4EbvK/eEzPQ9SmQPe++9FUHFPyP1VMJ/4BvrNq1ESOwaHwuaISUNn398PwZ292vhKGYZhWq4NXGGPDsMwDMO0E3bk7xAih3ik9yOYEjoFf1z8B66OvxoyiUyInCBpEOZUzBEiRy6XYa5yPSw6IyTdq4XIUZQkILC4B2S+Cug3vyZEjtkzECc6zxHH7Ts5kkUOwzAdgg5XdY3pmFCY2ObNmxvdRhNf0sIwDNORqTJX4bEtj4nXlwZdiu0/bscW2xZRPGZgt4EYNXAUtuZvhW2vDRW6CpGfeEVUOUKOHMR3jkSExmaLz4amzIXMQ4rqpY/CVpIHWXAwTk5+DqZME/wj3DFgWm1IMcMwTHuHhQ7TLvj0009FQYDGoMlOGYZhOjov7XwJhYZCRGoj4ZPqg1JbqVjvLP7iCuXyXdmlGj473seB8hB49K8NbXPPHwy36ijodrwpRI5br14ouewJZK8uhFQuwfjrEiCTc7AHwzAdAxY6TLug4fw7DMMwFxJUQe2P9D8glUhxuexypJakQqtW4boZo5BjVItKi1TRkuY+o5zVuYHpUO94C2abDAe04YgIz4DDJkNA6iWw5B+AreAYPKdOQf7Y27Drz9riJkMvjoFfmHtbXyrDMEyrwUKHYRiGYc5jSmpK8Mz22kmY54XMQ+rWVPF6Rs1iBCx+CQHRI9Fn6CJYZ89GcXExgva/BenOj8U+2z2vgn9k7RxjvtnjodD7Qn/0HXhfdhlyBl6LXX9kiG0Dp0ej97jaec0YhmE6Cix0GIZhGOY8hQqjPrntSVSYKpDglQDZsdrJf/uostDNVCtSkLFJLPLABIT4dgaS/hSr9wTdirTcrYj0NwEWNfwypsOSsQHyQC1OJl6JvX+LnEEzO6P/lKi2u0iGYZhzBAfiMgzDMMx5yq8pv2JTziYopApMt05HRXkFvFTAJOMfOGjoiqN934Vt0EJA6Q4UHasVORIpdgQsxK6juxE2rDZ3xz9jGqQGKcypy1Fw8ePYuzJHrKe5cljkMAzTUWGPDsMwDMOch2RXZ+OV3bWTD98YeiNSN9eGrM2y/I7USm/skLrDvvpLbJdEY8iMjxCvTIEkeSW21gzA4aTtiJmWBZnKDnl5DHyyJsJ0Ygksc2/Bwb0GcZxhc2KQOL5Tm14jwzDMuYSFDsMwDMOcZ9jsNlFK2mA1YIDvAOj368X6QR6FCC7PxmK3noiZWFtEoCKjFOsWZ2Gne2cERY9HVupydJmWDZnSjqqyWPTbfy9QXQmpWxGOWOIBGNBrTDiLHIZhOjwcusY0m6ioKLz11lt17yUSCX7//fdz1oMbNmwQ56ioqMD5cI2ZmZni/YEDB/6z9jAM03HJqsrCl0e/xIJVC7Bw7UJ8evhT7C/aD7PNjC+PfYl9RfugkWkwtHQodDodAjyUGFf9IzaVxyBw8D/lpL2jqxE3Nx3qTnuRm/UXukytFTnVZV3Rb/99kNrchDdHd/XDKMszQKWRY8BFPFcOwzAdH/bodGBGjx6NxMTEeoZ7a5Kfnw8fHx90JHbv3i0m2WuMiIgIcc3+/v51QmzMmDEoLy+Ht7f3f9xShmHaI5mVmViathQbMzeirKgMviZfeJu9UaAswDte78AhcUAlUwmPDnG9+/XIOZADuVyGOeYfUWTQoLSzA0GeFsgMXgjcNhdHeqyDX3AaAnuXiYUwlcah7/67IbWrYDq+BB5zx2LL9mqxrf/UKLhpFW3aDwzDMP8FLHQucKiij81mg1ze8luBZuPuaNAke6dDJpN1yGtmGObcU2woxgf7PsCRfUcQpgtDd3N3SF2CKiL0EYh3xGNHwA4UWgvFuvFe41FwsNZzMyWwGP65mfje3BfBvU6KdUEnroWHvS+GHhqG3JM7kNv9d3i5FwAl8ehx4C5ILFLU7PsE7iM6Iyt4BAx7MuHp74aeo8J5yBmGuSDg0LU29LYsWrQIDzzwAHx9fYUB/dRTT9Vtp3CtBQsWCMPb09MTY8eOxcGDB+u2X3fddZg1a1a9Y951113iuM7tGzduxNtvvy3CrWih0CtnONjy5cvRr18/qFQqbNmyRUw0N3PmTAQFBcHd3R0DBgzAmjVrmrwG17AuarvzPK7LF198Ibbb7Xa8+OKLYiI7tVqN3r174+eff653vGXLlqFr165iO3lKqL3NpbS0FFdccYWYWFSj0aBnz574/vvv6+1TXV2Nq666SnhsQkJC8Oabb4r+on47XeiaK66ha/Sa2kiQV4vWU59/9dVX8PPzg8lkqvdZGqtrrrmm2dfDMEzHQGfW4Z1972DBVwugW69D97LuwoNDIsfdwx0JCQkYPnw4FAoF5BVyTCmYgs8GfYZXh7yKiPQI8TCqR4gaffO+xJ6yMHgMKIJEBrgX9YF7USIc5lQ4LDUIqxyMAdufR/j2/2fvLsCjurY2AH9zxj2TycRdSQIhuFuBeqEGdSj13jp1v3V3/+tAC+1taYFipbS4FdcE4u7JJBm3/9k7JA2etNDYevucpyNnJiczJDnfrL3XfhKJOx4AbA5Y170BwxWjoL33EexYUciPZ+jFcRBL6U8/IaRn6HYVHfZHweVydcjXZn+o2AlvW3399deYOXMmNm/ejI0bN/IT5REjRmDixImYMmUKP+FngUSv1+OTTz7B+PHj+erXLBidCgs4bN/evXvj2WebFppjoak5PDzyyCN4/fXXERsby0/UCwsLcf755+OFF17g4YedsF900UXIzMxEZOSpu/I88MADuO2221quf/PNN3jqqacwcOBAfp2FnDlz5uDjjz9GQkIC1qxZg2uvvZYf05gxY/jXv/TSS3HHHXfglltuwdatW3H//fe3+bW02+08uD388MM8GC5evJgHi7i4OAwePJjvw17r9evXY+HChTzQsePbvn07H97XXmwY248//ojLLruMv0bsa7L3SyaT8QDLvgZ7D5mKigp+PL/++mu7vw4hpOtalrsM7//xPmLKY9DP0Y/fptAocPZZZ/PfTex3ezP24c+8efP4hzbLvl+GwMBA1NfXw6BV4cKyt1DvlCPD6I/wkBKI3DIEZlwLibYGwY9fj6pPvkD9skzIYs+C2hcHT30JbFs/RsgT90E/aRL+mJMBt8ODoBgd4gcEduArQggh/65uF3RYyHnxxRc75Gs/9thj/ES3rdLS0vD000/zy+zk//3338fKlSv5CfOWLVv4CTILHQwLJax6wqogLAicCvsDyo6FVTeON9yKhR8WqJqx8MT+0DZ77rnn8NNPP/ET9jvvvPOUX49VgdjGbNq0CU888QQPcixoseoGe09YhWjYsGF8HxawWCWJBTgWdD766CP+h/+NN97g9yclJWHPnj145ZVX2vBKgldyWNhqdtddd2H58uX4/vvvedBh1Rx2PN9++y0PjMyXX36J0NBQ/B1sGFtz4GQnJK3n6Fx99dX8uZuDDgt4LCw2V9sIId1bcXkxPlnxCcwFZgx0Nn3YI0gEjBk1BsOHD4fUUQdU7QEOZgJVB4HqbJgCe+Hm6+7EguWrceDAAZSVlUEQBExxz4fca8US52gEj8zkzxWQfQnE9VKYnh7HP1wz3XYjlKlrUfzQExAUofBaChDxzutQDxuG6uJGHFhfwh834rL4dn0YRwghXV23CzpdCQs6rbHhVCzcsCFqrMMOGwLVms1m40PMTofmSksz9vXY8DNWeWAT7t1uN/96BQVN7Uvbiu3Phmmx0DF16lR+W1ZWFqxW6xHBinE6nejXr+lTTvaHfciQIUfc3xyK2oLNM2JhigWb4uJi/twsYLGgx+Tk5PAQ3FzdaQ6DLFCdbjfffDMf+seOgwUwNnyPVevoBIOQ7mPdpnXYuWsn1Ao11Eo1/2CJVfUP5R5CXVUdH5pmgAEQAX3T+2LCWROgrdoBfH0eUPTnsU+YtQKKnXMxdfzT2BA2AZu3bMY4yU6E1hzABls/eBKKIFF4IGsIh6FgItRDRJDom36/MZpRoxAzbxbqfvgR+knPQp6QAKfdjVXfZMLnA2L7mRAST01TCCE9S7cLOuwPDausdNTX/if7sxNhNpeFhQ4Weth8mqM1Vw7YJ31smF5r7Rmyd3RnMRZMVqxYwStH8fHxvKp0+eWX88DQVhaLBZMmTeIBpXm4HMO+H4aFKHbi31pzxeqfeu211/hwPTa/hs3PYd8fm3vTnuM/XVh4Y9UxNvzv7LPPxr59+/j3TgjpHorLirFi2QqIIEIVqo653wsvzGozRg8cjXMHnwt1fRbw83Qge+XhPUSAXyRgSgICEgF9BLD1C6AqE6JFd2FE2ECMiIoF9ixEniMMh7QuhCfUAz4RgvdPB5xl8L/iqmO+riwqCoH3z+SX7RYXfnl/F8pz6yGVizHskrgz/roQQkhn0+2CDgsL7Rk+1hn179+fD1tgndDY5PjjYXNb9u7de8RtbJJ86/DEXgdW6WgLNneFVR0uueSSlnDSnmYALHSxOTcsqM2ePfuI6gWbbMsCDav2sGFqx5OcnMyHybXGhsC1FTt+1kyBHQPDjoPNUWJfu3moHHttWPvo5jlHZrOZ7zN69Gj8Hc3/zo73GrNGEix0sarOhAkT+JweQkjXx37XffHDFzzkVCgrkK/Oh8QngcTbtFmkFsQmxOKZMc/AYK0Flt4F7P2x6cGCBBhwPTD6IUAbdOQTD7oR2PwxsOploHgr3+pdcvzhC0fEsKZKvungVCiqIxBwYzy/7rC5IQgiHmRas5gdWPjOTtSUWCBXS3DRXenwC/yr+kMIITUuN7bXW7G93oId9Vb+gtwaYcIYg7ZbjUDpdkGnO2AnxqwqwoaAvfrqq7wTWUlJCa8KsCDChp2xLmysisGqBmxfNg+EBZ/moWAMC0ms0QELLGz+zMmaGLA5QvPnz+cNCNg/8CeffJKHhbZiw97YHBw24Z6FpOYqDhseptVqecXovvvu48/JOgyxkMHCCZvEP336dN7IgM3PefDBB3lI2LZtW0vHtrZgx8/mL23YsIE3V3jzzTdRXl7eEnTYMbCvw56fvQ5sXg2bH8UqY3/3BzoqKoo/9pdffuGNHFgVrHmeEpunw77nTz/9lL9HhJDuYemmpfBUeeAReXDBhRdgdMJoZNdlI6suCznmHCT7J+NC0yCIVj4PbPsK8LqbHtj7cuCsxwH/WLidTlRlH0JZ3h5Ulf6JhoaDkElCkTxgGqL/swXCyv/CvftHLHaNQOjYfRAJgLZ4CAz550JiLIEyZSKqSxox/9Vt8Lh9iEjxR2y6CTFpAXy42oJ3dqK+0gaVXoZJd6fDGNb0e4kQ0n1YPB4sqqiDXiJGmEKGcIUMBknThx6FdicyLHYcaLQj02pHrcsNp9cHh9fL/1/n9qDAfuyIlz9qGjDMT41HY0Iw2K97/N6goNMJsZNn1mr58ccfx4wZM1BZWckbCrDKA+sWxpxzzjk8jLD21Kzj2A033IBp06bxCfzN2Ik2O7lnJ/tsvk1ubu4JvyYLBuw52ERZtiAm617GOv60FWtlzcINe3xrbFI+qxSx5gasCsW6r7H5MmwIHqtcNQ8zZFUW1sWMhaH33nuPz6Vhc27YMbUFa37Anpe9LmxeDmvYwIIiC1Stv0cWqC688EIesNhrx7q9KRQK/B1sGN4zzzzDO9ix94m9/s3hjAU81pGNhdOj24ATQrqmems91v2+DjLI4Au14IKSzUB9PtL1YUjXxwCmQcCO2cC8WwBX0yekiJ8AjH8aCElDyaH92P7TNLjEu6Ew2CDTuiEKBXR8x53Ye/B3rF8ci5ikqWgIjIU2cHbTvJzaSAQfuBE+Sz4Cn7ocHpcXKz7fD6e9qZqct7uKb6LD1R2nzc3Xy5l0Tzr0JqrkENIdPZxZhB/Ka4+4TSkIEIuARk/bPqiOU8rRX6/CAJ0auVYHviqpwsY6CybtyMIEow6XBhkQIJXAXyqGUSaBUSqBTOha7elFvqMnenRC7ISbnTiyk1Z2gtoaO8lnJ/BsfZa/e8JKeiY2p4iFFVZJuvHGG0/787PubqmpqXj33XfRldHPGCFNXpj9AlzZLtilNjwofA+jo+bEL03YQGDCf4GYUSg5eABblr8LceAaqEz2I3azNcjRYNbBP6gGEmlTcLFWyuFxiKENt0KwaxG9+VmIa10IemA45JEhWPfDIez6rRAKjRTn3tIbxQfrkLOjkndYYwzBKky6px80htMzB5IQ0rlsrmvE5B1ZbLYf+mpVKHY4Uel0/zUHXCRCgkqOZI0SvdQKBMqkkAsivrGgwgJRskYBg/TIekeJ3Ym38svxbWk1PCdIB7eGm/BMwpHzrTtbNmiNKjqkx9ixYwcyMjJ4tYj9YDQ3TGBze06n2tpa3kiCbR9++OFpfW5CSMdYk7kGjmwH76Y2yFgIY1kNEJAEBKUA5iLAXAw0ljXdxoao9boQFfm5WPvKvfAZVsOQ2lQh9zkEaLclQFsRA7k9HlJ5FERKfzh35eNg+AKIUvZBZTq84LBHjPBd90DSIIXh6mgecgoP1PCQw5w1LRlhiQa+Db4wBnUVVpTnmBHVJwAKdfua4xBCugaPz4fHDxXzy9eGGvFaUtMcYLvHi1KHC06fD7FKOaRC+4flhypk/PlujwjEx4UVyLY6UO1y843N6WHhRyPpWhUdCjqkSzjvvPOwdu3a497Hhr+1tdMe6yrHFvhkjQTYAqPsOdlQvdOJzZNiYYetAXQm2lcTQk6NDVZg1X7WhCQ8PLzNc/GqbFX4ad9PqG2ohUqp4u2jNQoNNvyyAXro4dNZcGnZYkAsA6bOAgJ7/fVgrwcQxLBbGrHm3SdQ4/sVxvRaCFIfb/GsPZiCwJJbIHX5AWxEWatRZXIhFn3K74OrrAKHwn6EKCILYXlToaiNhnqoB5qhabA3urDyq/18/9TRYXxOTmus4QA1HSCke5tdUo29jTY+N+eRmJCW2xViATGq01PFjVXJ8erhANXM6/Oh3u2B0MUaFVDQIV3CZ599xucZHc/JmiwcHUBYk4MzrT3d6gghpx9bt4vNj2Ot3Zt/R6Snp/O272yow/EcqD6AOdvmoHRPKcIbwnnlxgprS/toFnK8Ii9ucv3Kh4vUD74VJcWboWkshzEwFQq1Ec78Auz4/i0Ua9ZBl2KG6XAzNFlZKIJzboayMYZflyf4QRamgcSohMSogNhPgfqVu2HZXAGpJBApJbcDTWt8QhJYCv+pU3lwW/VNBixmJ/yCVBhxeVPnNUJIz8GqKq/klPLLD8UE83kz/xZBJILfUUPduoKud8SkRzp6/R1CCDmeQ4cOYcGCBbw5CqvisDb9NTU1+P333/HHH3/wVvOsY6LaT40GeQOK3EVYdWgVXFkuRDVGIRJN7efBRn6xIe+txqmnBlQjsjIPTlMK1jt+hqzWBrC5wHmA2yaG2yKGfKATfoc/8BQqIhBcMAWamj68HbUi2R+6CVE85BzNf8pg+F3qhXnBRjSsLoCgDAdE2Qi6fzq/P2NjKbJ3VPJ20hNvSIFUdmRLaUJI9/dyTilq3R4kqxWYHnp6R6N0VxR0CCGE/GNsPSm2QC/bHA4H/z9rEHO6h4aeCPt6rL391q1b+XWj0YhLL70UWoMWa7evxf7d+2GpsCA7O5tvzdwiN0J9obyCwwRGBmLS2ZP4cDd+v9vNvx9v1u/Q/HQtWK/ntcGBkGkq4HNJ4POKIMhdkCg9fGPEpckIK7gMSnNT1UWeaIB+YhRkEdqTfg+CWIDh0hHwu2Q4nHmFkEWP5GGtIr8eq+ce5PsMnhSDwKgTT7wlhHRPexqsfNga80JCOCR/Yw5OT0RBhxBCyD8aJsYab2zdthXeo1qa+uDDpEsnYUDagL8dnlh3xIaGBt5Vh62Hdbx9du3axY+huSU+aziSPDQZj21+DDsrd8Lr8wJqQB2uRpglDDqXDjqnDnqXni/2yYRHheOcCec0Le7bWAEU/slbREtcNkhcFmDlk3y/gj6T4dOv5cPXwvfcDU1VGjwSK1yKatgVlVDagiG3hILlJmW6CdpR4cet4LRe3LM8px6GEBUMwWp+Gws38pimypKlzoElH+7mLaWjehvR7+yov/VaEkK6Lt/hBgSswHxxoB+GG7rHGjf/Bgo6hBBC2o0FjD+3/YnfVv4Gt+OvtqYeeOAW3DzkKLwKLFy4ELERsXwh35P9EWfDy/Lz81FQUMAX+2Xhpnnh4WZsyBlr2c7WBlOr1byL4sqVK1FVdXgejV6PSZMmoV5bj+m/Tketo2mNCYPcgARDQtPml4B4Qzzi/eKhFCt54xD29XnlqbESWPwAsO3Lvxb6bMVtiMJ+yVZIRYC+aBRUtamQhqghmCUQN6qgaIyASCaGemQwNCNDIfE7dskDtqBn0YFaFGU2bbWlFn47WwNn6MWx6Dchkl/mX8/pwZKPdvN5OYYQNc6+MZUPXSOE9CzvFVRgi9nC20I/FRfa0YfTpVDQIYQQ0i67Mndh0eJFcNc3hQGz1Iw9xj1QmVSI8otCtD4aOqkOu5fshtFhxFfffoW7br2Lz5dpjS2GzBYbZg08jg41zVh1g4Uadj8LQmxbunQpX3SYhRRGqVRi1KhRGDRoEJYXLsdTy5+Cy+tCijEFr495HeGaE3ddY0Pc4LQCa14D1r0DOBua7tCFA3INIFU1bQodNsiskGr2QmIzwHTwKgTOSIci3o/v7nV64Kl3QqyVQZAfO3/G4/Fi/9oS/Lk4F7YGV6tvENAZFaivsmPj/GyUHKzD+OuTeXvo32dnoCK/AXK1BBf8pw9kSvqTTUhP83t1PV463IDg+YQw3gKatB391iSEENJmyzYvw8alG/nkeqfgRJ4pD8OHDMcjSY8gSB10xL7P1TwH61orzJVmLFm2BJMunNRy3549e3i1x+VqOukXi8W86UhkZCSfH9M8VI2FHEEQUFdXh/3792Pv3r0oKSnhIYcFp2HDhmHEiBGQy+X4YOcH+GT3J/z5xpsG4MXCbKjeH96UJljOEQlNm1wLKPR/bUVbgYamEwmE9AUmPgfEjjnieynJWgxXwd38cvD+G6DqHdkSchhBJoYQoDzm9WLVouztldi0IBvmiqbOkVp/BaL7GBHWy4CwBAMPMvvXlWDtd4eQv7ca3z3/J6L6GHHoz3JewTn3lj7Qm1r1oiaE9Ah5Ngdu35/Ph6xdF2rENaHGjj6kLoeCDiGEkDbZfGAz1i9bDzHEMBvMGHf2ODyT+Ayk4uMvTnnviHsxI2cGehf2xvat2xEbHYtevXph2bJlLU0DoqOjMXbsWB5y2Jo3J8IqOMOHD+cbCznFxcU8FLFAVGYpwwu/v4BVRav4vjcY0nHP1l8geFtVTlqzNk3oPYI+Ehj/FND7MvhEPric1XC76+FiW2Ml9u59AGIdG7I2GorGvjBOOnV755JDddgwPwvluU1zh5RaKV/YM3lkKMTiIxfdSx0VhqAYPZZ/uhd15VZe/WFGXZGA8KQTD/sjhHR97AORo6vOFrcH1+/JhdntwQCdildzSPtR0CFtxk5I7r33Xr4x7Ifyp59+wsUXX3xGXkU2uXjcuHH8pIad5HRVZ/p1IuTfkFGYgYU/LITUJ0WjvhHP3fYcNGxoV2tsVUwWIsxFQH0JtFIFbht/Kz776XP0MvfCzwt+RsC6AJSVlfHdR44aiaA+Qci152JH3g7UOer4ZnfbIRPLIBWkkIvl/HK0LhpDQoZAJVXx+T5sc3vdmLVvFq/kWN1WSERiPCky4dLtC/nze+MvhHfgXRAbjeDnEOz42NwbRwNgNx/e6poqPKmXoNFRgINbZ6K6dhkE6ZEhiYWcpiFrV8I0JQGC6sShrKbUgk0/ZyN3V9PcIYlMQL+JkUifGAmZ4sR/dgPCNZjy6ECsnpuJg5vLkXZWOHqPaer+RgjpHlbXNGBDXSMK7U4U2pwosDtQ4/IgXavCBKMOEwJ0vH30fZmFyLDYESiT4PPeMZALR344QtqGgk43xj4lZYvkvf3222fk+UtLS086wZgc+zqxuQgxMTHYsWMHf28I6QqKq4vx9eyvIffIYVFa8NCND/0Vclh42PktsP4doDYP8DiOeOz4C97EwrRAVP5ZCZPdxEOOXCmHIl2BV2pfQdnSptDTFjJBhsEhgzE2fCwitBF4e/vbOFBzgN+Xro3Gk/mHkGjeAojlaAi7BQeXLIJjzQzI3DIotcFQGCMgD46BPCER8l6JEIexqowXdXU7kb3qQriEHP5cwuEM43EK8DgEeJwSaD3hCMu+FvKYUCjTTLBbXKgsbIBYIkAiZZuYN2DY/UcRDqwr4S8LayqQMiIEgy6MgVrfthXLWRCaOCMVo6Ym8nk6hJDuY1FFHW7ed/xFxf+st/DtpdxS+EvFPPxIRMBnqdEIltPvgr+Lgk4Px8qlrHvS0ZOE2yI4OBjdFVuTQyY7PRP+uvPrRLq/Oksd3vv8PSicCtikNtx+/e0I1AU23VmTAyy6F8hdza+yceQeAXDrTHCpdZBU50K57FE8dt3/MLXoSfQv7g+JXIKlfkthLbPyx2hlWkRpo+An94OfSAI/jwdKtwNujxMOrxNOjws2rxM7XXUodpmxrngd35ppxUrMdEhw6e41TSvhBCShJmQ6tpV+AOGmptBlgxNmZAF8+6PpQJvy0V8EgHWhdheFIbBwPHT1gyERKSAR/vrd6JWIEHBZAlwOD+a/tg21h7+H44npG4Bhl8S1tIxuLwo5hHQvdS43HjtUxC+zys1wPw0iFDK+aSQC1tc2YmV1PdbWNvCQwzyfEI7BftRK+p8QuueJu7VDNva121Ntufvuu/HQQw/B39+fnwz/97//bbmfTby96aabYDKZ+Bj0s846i68V0ez6668/ZigUG1LGnrf5ftbN6J133uFDp9jGqglsOBi7zLoWDRgwgE/gXbduHV9Ab/LkyQgKCoJGo+Hdi3777beTfg/seX7++Wd+mR1789dpvX311Vf8fq/Xi5deeolXM1iHpL59++KHH3444vmWLFmCxMREfj8bssaOt61YJ6aLLrqIV07Y5GXWgpY9XzM2gfm8887j3xv7Hq+77rqWlrTN78edd97JX0PWZvacc85p8+NO9j4e/Tqx75/p168fv509fs2aNXxuQvNwntbvJ+skRUhHYT+3r37+KhRWBZxiJ668+krEBsUCHndTBefD4fDmrcaBMANWDgnHylEmrB4ZgPVpPmyJM2P9QH8UqgQEL7oft/afgZVhK7E8YDmsEiuGhgzFq/3uxx/KvphbXIKPtvyMl9bNwsMbv8Hdf/6AmdsX4tGdy/D0npV4ed9aLD24Bz8VleIelxLpMiNUggwXepVYlHMIlxfugyBRAMPvQnbIRdgqeQdCigM+lrpKI+GtDISnTgePVQ6vS+Abq9a4HQLcdjGcdQqI9gxG1B8vo3fGCwi0nAWFWHNEyIFYBOPkeIj1cj60jIUcqUIMvUkJjUHOg4lELkZIvB6XPNAf59+e9rdDDiGk+3kuuwSVTjfiVXJepflPZCAuCvRDuk6FeJUC08MCMCstFgdG9sG3abGY3ScG06n5wD/W7So6Xq8Nq1b36ZCvPXbMHojFbe+M8/XXX2PmzJnYvHkzNm7cyMMJ6x40ceJETJkyhZ/ws0DC1ob45JNPMH78eBw8eJCfUJ8KCzhs3969e+PZZ5/lt7HQ1BweHnnkEbz++uuIjW1a36KwsBDnn38+XnjhBR5+Zs2axYNDZmYmn/B7Kg888ABuu+22luvffPMNnnrqKQwcOJBfZyFnzpw5+Pjjj5GQkMBP7q+99lp+TGPGjOFfn61ifscdd+CWW27hE5Xvv//+Nr+W7HGsCsOelwUd1p2JhZPm0MiCIguOb731Fmw2Gx5++GFMnToVv//++xHvx+23347169e3+3Eneh+PtmXLFr6YIQuRLIyxqhF7P9n7MHv2bDz44IN8P9aJir2Gr776aptfA0JOtw9/+hCyGhk8Ig8mXjwR/WL6AbZaYPal8JVsR4legQNxIRBp2HwWO29sxng9gM8tQCz34kCiDkE7s3DlwQ2o73s7L6ZMipuE8PzNwMK7AWerttJiGWBMAAzRgFTRdJ1tggSikh2IL9mO+KJM3NT6IMVyYOiN8A67E5uX3weLcRvLJBA3BiB8z51QNESf9Ht0e10QiyQtE4HFRgU0Q0OhSPCDSC7m6+IIMgGQCHyfAxtK+PwZtvuFd6QhNIGG7xJCTm5DbSO+Ka3hl19PioDiqGYkrbH7zjLq6CU9Tbpd0OlK0tLS8PTTT/PL7OT//fff54vfsYDDTogrKip46GBYKGFVAVYFYUHgVFg4YifRKpXquEOnWPhpfSLOTrZZlaXZc889xyfQs/avrNJxKixUNAeLTZs24YknnuABgAUth8OBF198kZ/cs1awDDuxZ5UkFuBY0Pnoo48QFxeHN954g9+flJTE28++8sorbXglwRcZvOyyy9CnT5+W52/GXldWQWHH0OyLL77gK6CzMMiqSM3vQetg8fzzz7fpcSd6H48XdFiwa167o/X7cuONN+LLL79sCTqLFi2C3W7noYqQjrB251pU7qnkbaRDBoRgXJ9xgNsBzLsW9dW7sC0+GN5QN0RwwWWVQLqtH/SlcZB7AyAXGyFWqVE05nVAU4GNESaM2fM9bo8dy7uaYfljwNbPm75Q1Ahg6O3wqqNRuXELiv78Do3u/RCrtZDo/CDVGyDx80dg/zsQdMUICLl/AAeXA2V7gPjx8Ay7Exm7Z6P4j3MgDrDxsKUtHIngg9dBUKihGR0Mb6OTr3HjMTvgaXDC52BJrOnLSw5PyFEkGaAeFgpFoqFlwc6j1ZRYsGbuQX558EWxFHIIIadk93jxQGYhvzwt1IihNBStcwcd9on5a6+9hm3btvFJ1qfqJtXcOeto7LFnYu6CICh5ZaUjsK/dHuwEubWQkBAebtgQNbY4Hl/IrhVWUWBDzE6H5kpLM/b12JCrxYsX8/fG7Xbzr8cCRHuw/dm/B1bhaT5Jz8rKgtVqPebEn1VgWJBgDhw4gCFDhhxxf3Moags2fIxVY3799VdMmDCBh57m15e9nn/88UdLEGuNvZ7NgYUN5WutrY870fvYHqwKxMIhC4lDhw7lQ/7Y68eqU4T82yqqK7B80XJIIIE1yIrbLritqenAgjtRUrwV+wb5Q5C64fWIgAO9kFB8K+QiP+Co5oihe29DweDn4Y704mCNFomL7wc2fQSUN/2Odo68A3vcjaj98wV4dDWQmDzA+U2PdaMBDjS1WGaq6hdh9yoxpKVBCNONQfDwm5Cx6wOY15wNsdbNu6LBoUDogRuhrRgEWawexqt68QU8j8aHGbu98Do88Dm9EEmF4+7XmsvpwfLP9sLt8iIi2YAB50adlteaENK9vZVfjhybA0EyCZ6IC+3ow+lx2h10LBYL/+T/hhtu4EON2ooNgWJzTZoFBh6ezHqasaEF7Rk+1pGOXjOCHTsbE89CBztZZiHxaM1tltkCekfPCWpeeK8tjj6BZsFkxYoVvHIUHx/Pq0qXX345DyPt+bcxadIkHlCah8sxzSuesxDF1sporbli9U+x4WVsXg37GizssKFyrDp011138a/PhuEdrzrEXucTvSZtfdyJ3sf2YD8P7Guxqg6bx8OGLB7v/SfkTGO/Rz6e9TEkHgnqFfV48JoHIbBFNn9/AZb9P2BvXxPEUg9cZf4I2387DO4EvhinNFgFeZI/JDoZn8ciUohR8bUIxtyLUB23AHm9lAjbXg11+R74lP441Odi5Dl+hFjphii86Y8R+5XmqJPD1aDnrQ0EtgleiAQvZKZ6SLQe+LQlKMJcFNXPBWIAMRsqZ1XAkH8OAkvOhcijhG5CJLRnRZ6wMsOHqUnFEEvZo//icXmRv68a2Tsq4PP44Bek4vNs/IJVvJsaq+iodDJMmJF6wucmhPRclU4XrB5v0/rEIhGK7E58UFDO73spMRw6yZG/c0gnDDpsYjbb2oudyHXltVD+Tf379+cT01knNLZ2zfGwIVBsonxrO3fuPOKkmw1dYx3V2oLNS2FVhUsuuaTlJL89zQBY6GJzbtgJPptr0nrhq5SUFB5oWLWHDVM7nuTkZD5MrjVW3WgPNqSMzRNi26OPPopPP/2UBx32ev7444/8tWxPd7m/+7iTae7kdrz3hYW1q666iq8Kz4bxsXk+hJxJOeYc2Nw2xOpjIRfk/N/lVz9+Ba/ZC4fgwMRJExGiCwG2z4Zn3avYkBAMscYNod6E5H3PQuxRQtBIoT87GqqBQcec/AdMSYJo7kVoDNgFhz4PGyJDMMwTic3yWkCxkIcUR60Cnqx4eG3p8HcOQKLXcNwuOW7BiSLdZliM6yAKzYFY6YK3QQND3oUILBsPwSeFoJPBf2oSFPFt/1vDwk1ZjhkHt5Qhe0clHFb3iXcWARNvSOFhhxBCWp8D/Te7BJ8UVh73RTk/QI/zTXQO3K3n6LA1Q9hcDTZngw2ROtlJHNuPbc3q65tWle4p2NArVhVhQ8DYnBE2RKqkpIRXK1gQYcPO2CR5NoSQNQ1g+7KJ/iz4NA8FY9gJOpsgzwILG351siYGbG7J/PnzeVWBhZQnn3yyXVUJ9p6yOTismsJCUnMVh80V0mq1vGJ033338eccOXIkzGYzD1esyjd9+nQeTlgFhs1RYSf8bGhkc8e2tmAdylgAZ68VW2CUDTlj4am5UQELPSxENHdHY8Pp5s2bh88++wxi8fE/Yfm7jztV4GfVMrYyPAs0CoWCv0YMq0ix14PNDWpdESPkTFi8fzGWL1gOnVMHwSeA/deMrQej7KfEeSnnAdm/w/fLvVgfHAghiC22qUDUrgchFqmgHRcO7dhwCHIJ3C4PBJ8IQqtJtqo0E2wZoQjZewvyhj4NIdyOTZ5DEIl98HlEkGYMRXzJDIh9RwYHQS2BiD1P08eiTfNpzEB03SigbhR82V40KiqgcZgg8okhaGXQjQ2HenAIH4bGeNxe7F9XgroKK7we3+HNC4/bB1uDExazE9Z6BxyWI4ONWi9DwqAgqHRy1JZbUFdmRU2Zhe83dHIswnuduhkMIaRnhZzHDhXjy+KmjqxKvrAn+y3a9KsrXC7Di4m08G+3DTpsiA/rtMVOzll4YSeIrKUuOwFnn5gfDxt29Mwzz6CnYkGDtUZ+/PHHMWPGDFRWVvL5TKNHj+YtjptPilkYYSfgbNI6G0o4bdo0PoG/GQsXLESwigqbb5Obm3vCr/nmm2/y5xg+fDhvr8y6i7UnYLJW1izcsMe3xoZisUoRa27AqlDsvc3JyeHVPfb+P/bYY3w/1tmNVU9YGHrvvfd4ZzLWBIAdU1uwT6JZMCkqKuJh4dxzz+Wd0pjQ0FAeqtj3dPbZZ/N/h1FRUXwfNgTwRP7u406GVYbeffddHmRYVzrWPrp5iBp7TvZase+bvZeEnCm/Z/6O1fNXw9997Ek767BWElKC9897H6g8CHx/PXZptXDFe+HzihCx527I7IEw3dwb8lg/lOWasWN5AXJ2VfK/6mzxTNZ2mS18GRCuwejL42HNqUPgoSmo6PUtDzneKhMiMu6Exto0z4UNe5NF6yGP0UEeredD347mNjtgP1AD+4Fq2LProLUHQ9BKoR0TAc2QYIhaDUNjFZo/5mTwoWZtIVdJENvPhMTBwQhN8INwnGFpLMixRUEJIaSZ1+fDIweLMKukmn8u80avCFwdcuT8atKxRL72LP5y9INFolM2IzgeNnyJndiyIU5treiwYUmsCtB6ng/DTvLZCTyb18A+HSekK2Pd11iwPXoYX0ein7HuZeOhjfj5u5+hdCvhUXpw23W3wS1xo6CxALmNuSi1leLK5CsRJdECn41Hvq0MhwaqeGHFtP8a+BdNhP6CGNT4K7FjeT6KD9ad9OvF9Tdh3DmRKP94J+oiVsDjFCOg7CyIIEAWqYX+wljII9vXSpU1EXBXWHlAah1wnHY3Ni3IwZ5VRTx0KbVS9BoaArFUgCBm1SYRxBIBSq2MDz9T6WVQ6+SQswpSq+G2hBByyt9DPh8eyizCnNKmkPN2r0hcEUIV338LywZsRMzxskGHt5dmn9az1sInwuZznK5J6oR0BewHlVXjvv32204Vckj3si1nGxZ8t4CHHJfShftvvR/+fk1/mKMCozAKhxeo9biAOZeh3pKPg/38IYh80BSMgKFoAmTJ/li+vhTleQ18V1b9SBwchPSJkVDr5TxsuBwePmTs10/3IXt7JeL6BSJwfDSE35oW4hXrZdCfFwNlX1NLwPB5fbA1utBYa4fb6YXaTwa1n/y4VRRBLoYsQttynT02Z2cl1v1wCI01TR+S9RoajBGXJ0ChObJZCCGE/FMOrxcPZxZhXlkNH/T7bnIkLg+mkNMZdUjQYZPmW3etIuRU2PybtWvXHvc+NvyteQhcVzV58mS+dhKbq3S89XcI+af25O3BD9/+AIVbAYfCgZm3zGwJOcdY/hhchWuwJdUEQeaFpCoaoZkzIAlQYVVBIyqLLZDIxUgdGYq+4yOg9f+rmt4cLIxhGgw4rxF/Ls7D6nmZuPKJwdD6wBffZOvVCDIxSrPN2LIoB+YKGyxmB59HczSFWgq1QQ5ThAYxfU2ITPGHRNYUfticm6xtFdi2LL9lmJrWqMC4a3ohIoVOOgg5mZ31VnxcWIFLgww4O6Bprig5te31FtyXUYhMi52HnA9SonBJEC0c3Fm1O+iweRhsQnYzNmyMBRc2UZsNR2PdroqLi/kkeebtt9/mw8rYKvBsCAybo8NWlWeT1glpK/bvhs0zOp6TNVnoKqiVNDlT2Py1//36P+zbsg9ynxw2hQ333novTAZTUz9nrwcQt/pT8Ofn8P75f9gYb4JI7wUsWkTtmQmRVIZtTi8POUqdDJfe35+3Xz6ZAedHI3d3FaoKG7H620yc/5+0lgrOgQ0lWPVN5pHhRgSotDIeoqx1Dr5mjd3i4lt1USMyNpZBIhMQmWpEYJQWB9aXwlzZ9HtBphAj7awI9D8nClI5zaUh5GS2mS24clc2Gjxe/FxRh0sC/fBcQjgCZD1vHXmn1wuJSAThFMNX2cKfr+WV4aOCCrBWTQFSCd7sFUEhsZNr97/orVu3HrEA6MyZM/n/2aR31iWLLTbZepFJtg7L/fffz8OPSqXiiyuy7lzHW0SUkBM5ev0dQsipFRQWYNYPs+A2uyGGGA2aBtxz/T0IMYQA5fuB764FarIBQQpIlU2bpQq7wv3gCvHB5xIjetdDkLh0OKSTIr+gkc9nmXxP+ilDDiMWC5hwfQq+f/FP5O2p5kElaUgQNszPxq6VTSuFx/UzNQ1785PzOTPsMQybPspaPVvqHGiosaPwQA0fnsaGpuXsqORbc8Wn7/hw9BkbDrmKhqkR0paQc8WubDR6vIhTypFrc+Cnijqsrm3A8wnhPPT0lDlrK6rMuPNAAQ865/EW0HqMNGggO9xwyOPzIcfqwO4GK1/4M8vaNDSWVcGeTwiDv7TnBcMe1YygM0w4oonShJxZ9DPW9bAPmBYvW4yd23dCBBFfE0fcS4zHLnkMKqkKKNsDzJoMWKuPeWxuoAo5vZpCTPD2O6CvGoRSrQxbCi28ajL5vn4IjGpf84Bty/Kw6ecc/vjAaB2KMmr57YMuiMagC2LavPgm+3PFqkMs8FTk1fNWz6mjQ3mHN0LIqW09XMlhIWe4nwaz02JwyOLAzIwC7LfY+T6jDBqcE6DHIL0aqWolJN1wcVz2u+TDwko8n13CW0C3ppMIGOGnRZnDhQyLDTbvX3sEyiR4LSmCvz6kY3XqZgSEEELO3B/w2d/PRmFWIQ85hdpCnHv2ubi89+VNn9KW7ARmXwyXqw4H0iNRb2Bjy33w+bxsVj8cnkqI4IM+8wIecswqKQ85bMjYBXf2bXfIYfpNjETOzioeTljIYS2ox1+fgvgBge16Hnb8pkgt3wjpblj1YIvZgnCFDOFy6WmvqvxptuCqwyFnhJ8Gs9JioBaLka5TYdnARHxQUIG38sqxtraRb4xKLKC/VsVDz2C9GgP0augkXXtoKBuC9kBmIX4ob/rA5bpQIy40+WFxZR2WVplR6XTz/zdj6+IkaxQYolfj7qggGKiK06VQ0CGEkG5kye9LeMjxwousmCw8fdHTSPJParqzeBsw+xK4XPXYnB4Ch9oKeKxHPF4kAPKivgjKvxxOpQRrS6y8LfP5t6UhNP7vrezNFhGdcH0yfnxtG58/w56LwgohR3oosxDflNbwy2z+Bwsg/bQq9NWpkKpRIFj298JPjcuNDwsq8HlRJa9OsJAzOy2Wh5hmbKjWfdHBmBToh4UVdTxwbau3oN7txbq6Rr7x3w8AktUKDPHT4OoQf/TRnnoIa2eSb3PgP/vzsa3eCrEIeDY+DDeEBfDXdYy/Fi8lhvOhfez7j1DKkKpRIkYph7iHDOXrjmjoGiHkpGjoWtexbf82LPx+Ia/kFEcU4/VrX4defniIRcFm4JvL4XI3YHN6KBxqBzx2Kewb+8FrF8HjccHrdSNckYZwjIdPKsbKagesXmDCjBQkDQn+x8fHWk+zag4LPoSQv3xbWo2ZGawKC34C7j7OpAJ/qZifeKdolEhQKRDBKj8KKcLkMiiO8zNldrnxcWEl/q+oEhYPmz4PjPPX4vPeMUeEnJOtE8M6i7GTflYNYv8vsDuP2OecAB3uiwrmoayzaXR7sLPBih31h7cGK0odLn6fXiLGp6nRGO1P1eGuioauEUJID1JYUYiffvwJEkhQaajEi1e9+FfI2fMDsOAOuH0ObO4bBofaDp9DjtjtT0DhigDYSJRWo1F8ImBjnYuHnEEXxpyWkMPQXBpCjrWnwYrHDhbxyw/HBOO2iEDsa7TxE3PWAnp3gw3ZNjtqXJ4jhpW1ZpJJoBWLoRSLoBAEPtxqd6OVV2SYPholHowJxkSjrs1VIdaFLFmj5Nv0sAB+W7nDxQMPG+bFKj/Lq+r5Nt5fhwdigtGvAwNPns2BNTUN/HXbXm/FQYv9mPk3LN4N1KvxVq8IxKlokfmegIaukTaLjo7GvffeyzeG/bL86aefcPHFF5+xlsusO19tbS38/P7ekJnWxo4di/T0dN7y/HjfDyFdldlqxgdffQCFR4EGRQMenPYgTCoT4PUCq14E1rwGt1iETekRfLgaCznR2x6HojEC0jA1W/UTPocbPocXXq8Pu6rtqHJ5kTgkiDcMIISc4mfQ5cbeRhsPKOz/FQ43rgzxx8WnWF+lzuXGTXvzYPf6MMGo43NAWMBgc2HY1szm8eKg1Y59DTbst9iQZ3Oi0N60WT1ePq+kEu5jnr+XWoGHYoJ5R7HTMecnSC7FRYF+fHsgxo6388oxv7wWK2vq8XtNPT5KiTrp98xeJ/0ZmOOytLION+/LO6YSFiaX8vDVX6fm/0/TKvm8JNJzUNDpxo4+sT/dWCtxA5/ITAjpKFaXFS98+QJUVhWcYieuu+o6RBuiAUcj8NOtQMYvcEpF2Nw/Fk65uVXIiUSJUoL9RRYotHIoNVIoA5UozapDo9WD0AQ/nHVtco9pM0vIiZp7HO9ngA3rYlWDJZVm/Fptbmk73Nqq2gYeAF5KCIfmOBP42XPcfaAA+XYnH4b2fnLkCddyUYoF9GXzdY6aE8OOj1V6ShxNgYcFJhaKbF4vDFIxRhu0p1wf5u+KVynwfkoU7o8OxvM5JVhcyVo15/PvlYW21hxeLx45WIS5pTW8ffXrvSJOW+BYV9uAW/fl85DD5jSx4Wj9dSqka1U8mJGejYJOD8d+SbIFBSWS9v9TCA4+PcNZCCF/T6WlEi9++SL0VXrWNw1nXXgW0qPSgfpS4JspQPkeWFRybEoLAWSHQ872x3jIyRWJsLu0abFNi7lp3HozfaAS593aB2IpzaUhPdcXRZV4JbcMUpGIBxE2OZ39v8HtwfIqM8qdR1ZQ2HyZPhrWOEDJQ8fHhRX4X1ktttRZeKWjf6sKDZs/8lFhBX6trodcEOGz3tHw+xuVDhbCjDIJ3zpKjErO57vcsT+fr8dz095czOsbh6F+Gn5/lZNVrXKxyWzh19k+bO7Pl31iEKWUH/FcbC7Ql8VVkLF1bUx6HtRYyDsRNrRv+p5cOH0+XrVix9Ed22GTv0/ojifuFo+nQ7b2LEnEqi133303HnroIfj7+/PQ8N///rfl/rq6Otx0000wmUy8P/hZZ52FXbt2tdx//fXXHzNkjA3BYs/bfP/q1avxzjvv8F+EbMvLy+PDwdjlpUuXYsCAAZDL5Vi3bh2ys7MxefJkBAUFQaPRYNCgQXxh15Nhz/Pzzz/zy+zYm79O640tIst4vV689NJLiImJgVKpRN++ffHDDz8c8XxLlixBYmIiv58NWWPH21bV1dW46qqr+MKibGHaPn36YO7cuW1+PCFdTXZ1Nl74+AUecliHtX7j+mFCvwmArRaYcykPOdVGEzakGQFZI3yNWsT++QwUDVHIYSGn1gmdSYlL7u+Hi+7qyxsOjLg8HoMvisHke/tBoaFPQknP9UlhBR47VAyz24Mql5vP+2BzUlgL5lkl1TzkaMUCr078X2o0Dozsja3DUvnJO5ur8lR8KH7qF8+HTrGKzUU7DuGeAwW4dncOBm7ch/i1e/BGXjn/Wi8mhB9TqelqWNXo3eQoXslhVaXrdufwRTbZOjTnbzvIQw57vf4bF8rnE7E1e87ZehCraur/Wt9nZzYu2n6ID4WbV1bDA0zq+r08JLHbKg43Emh2yGLH1buzeaOFkX4aHiYp5JBuX9Gxer2IW7OnQ7529ug+7SrFfv3115g5cyY2b96MjRs38nAyYsQITJw4EVOmTOEn/CyQsAWRPvnkE4wfPx4HDx7kwehUWMBh+/bu3RvPPvssv42Fpubw8Mgjj+D1119HbGwsH35WWFiI888/Hy+88AIPP7NmzcJFF12EzMxMREZGnvLrPfDAA7jttttarn/zzTd46qmnMHDgQH6dhZw5c+bg448/RkJCAtasWYNrr72WH9OYMWP417/00ktxxx134JZbbsHWrVtx//33t6szGAtuDz/8MA+GixcvxnXXXYe4uDgMHjy4zc9DSFewpWAL5n47F352P3hEHkycNBGj+40GXHZg7tVAxX7km0JwMMEDQeKEqDYIcbseg8SpR5ZIhH21TuhNSlw8sx80BpqQS0hrHxVU4JnsEn75nqggXGTS825jzXNi2PqR7IR+hEEDuXDiz4tZC+bfByXh4YNF+LmiDt+VNbWObhYkk+C60ADeprk7kAoiXlFha/VsOrwwqdPr4+v2RClkvKV1olrBW1jfuDePh8erd+XwuTOs3TMjEQFTg/15Vzg2LLDE4cIvlWa+MTFKGQbrNXxo2jv55XzYXl+tEl/1iTlu5zlCul3Q6UrS0tLw9NNP88vs5P/999/HypUrecDZsmULKioqeOhgWChh1RNWBWFB4FRYOJLJZLy6cbwhZiz8sEDVjIUnVmVp9txzz/FGAwsXLsSdd955yq/HqkBsYzZt2oQnnniCBzkWtBwOB1588UVeIRo2bBjfhwUsVkliAY4FnY8++oiHkjfeeIPfn5SUhD179uCVV15pwysJXslhYavZXXfdheXLl+P777+noEO6DVY1/nHXj9i0eBP0Lj3cYjeuuOoK9InvA3g9wPybgIINyDAEoijJxXoMQFqegOi9MyHyKHBAJMJBFnIClbj4vv7QGI4cNkJId1dgc+Cn8jr8VFGLCqeLB5ZLAg0YZdDyasD7+eV4PqeU7zszOggPRgfz0Qm9/2bFhU28Z5UGtiAl61YWq5LzBgFJakW3XHiSDTOblRaLy3dkYXdj09DYYX5q3tLa//D3G6qQ4ef+8Xj0YBG+La1pWdOGBZx7o4JahrM9Fx+GXQ02LKmsw4rqemRY7Mi1OZFrq2kJjQkqOb5NizvuHChCmG73U6YSBF5Z6aiv3d6g01pISAgPN2yIWmNjI4xG4xH322w2PsTsdGiutDRjX48NP2OVENZkwO12869XUFDQrudl+7MhdSx0TJ06ld+WlZUFq9V6RLBinE4n+vXrxy8fOHAAQ4YMOeL+5lDUFmyeEQtTLNgUFxfz52YBiwU9QrqDdRnr8NOKn6CuVkMDDTxyD269/lZEhESwBAQseQA4sAiFGh0KU3w85KgLByEs41a2Yie2ekUoqaeQQ3oeNhF+XmkNfiyv5WGjte/LavlmlEp4lYCdUDMPRAfzIWinAwtKFwb68a0n0EnE+LZvHB7MLOSLbT4SG8wXJG2NVcLeSIrAMD8N9jbYMCM8ANFHzddhrxtbn4dtj8WF8o5tW+ut/D3cXNfIW0d/mBLVofOTSOfX7f51sB+MrtI6UCqVHnPsbC4LCx0s9LD5NEdrbrMssHawR80JcrmOHL96Mmr1X5MiGRZMVqxYwStH8fHxvKp0+eWX88DQVhaLBZMmTeIBpXm4HMO+H4aFKFZ5aa25YvVPvfbaa3y4Huswx+bnsO+PzVlqz/ET0hntyNyB+b/Oh7haDC2aFreTmWS4/drbYdAf7nq45jVg6xeolMtxoLcCYrEX6tL+CDtwO9xSAWtq3Wh0+2AMU+Oiu9Kh9qNKDukZ7B4vrt+TyzugMWya+gg/DS4NNiBSIcOiijosqqxDtcvdEnJYO+aZ0dRs558IkEn4fKWTYec8U4L9MSW47dWx8UYd3wjpsUGnO+jfvz/Kysp4JzS21svxsLkte/fuPeK2nTt3HhGe2NA1Vuloi/Xr1/M5QpdccklLOGlPMwAWuticGxbUZs+efUQ7zpSUFB5oWLWHDVM7nuTkZD5MrjU2BK6t2PGzZgrsGBh2HGyOEvvahHQ27N/noUOH+BpRbJjm8dq05+bnYt4v8+CodEAMMe+q5gv0YcrZU5qGqvEn8gArngI2vo8GqQTb+/hDInNBUROP0H23oVEqxtoqJ1w+8EU/x1yTBKmsa3wQRMg/5fR6cdO+PB5y2AKabMHMS4L8ECKXtewz0qDFCwnhWFfXwOeE9NEq+bwZQkj3QEGnE5owYQKvirAhYK+++irvRFZSUsIrIiyIsGFnrAsbq2KwpgFsXzbRnwWf5qFgDAtJrNEBCyxs/szJmhiwOULz58/nDQhYSHnyySf5yVhbsWFvbA7Or7/+ykNScxWHzRXSarW8YnTffffx5xw5ciTMZjMPJ6xxwPTp03kjAzY/58EHH+Td5rZt29bSsa0t2PGz+UsbNmzgJ41vvvkmysvLKeiQToUNB92xYwc2bd6EenPTp8fLli1DaEQohgwcwgN/ZWUllqxYguK8Yn4/66hmCbRg6tlTMSS+1fBOtk7O/JuBzCVwiEXY0DsYEpUdkvpghO+8D41eGVbXOMEGv4+ZmoDU0WG0Jg7pMVxeH27bl4/fquuhEESYnRbDQ83xsLk5Y/11fCOEdC8UdDohFjRYq+XHH38cM2bM4Cc+rKHA6NGjeftn5pxzzuFhhLWnZh3HbrjhBkybNo1P4G/GwgULEayqwU6wcnNzT/g1WTBgzzF8+HAEBATw7mX19U0nYm3BWlmzcMMe39qXX37JK0WsuQGrQrHuazk5OXwIHqtcPfbYY3w/1tntxx9/5GHovffe4w0E2JwbdkxtwZofsOdlrwubl8MaNrCgyAIVIR2NzVH7/fff+fy75iGmDsGBemk9AhwBKCkswU+FP2HRokV8flxzwCnRleDss87GlL5Tjgwp5mJg7hVA2R44ZXKsSY6ARFsHkVWHyB0PwelSYUO9CyqDHOfe0gdBMXQCR7o2NmqATUxfW9vAF4IcbtBAfIKFMN1eH1+4ckmVma/H8mXvE4ccQkj3JvK1Z/GXDsJOuFllgJ20sgpAa+wkn53As/VZFApqk0rI6UY/Y/8Mm7vGOhCyRiOMWWZGli4LohARbuh7AxbtW4T63HpENUZB49bwIWoFmgKok9R44qwnEKw+agB73nrgxxuBhlKY/QOwOVYFscoKOOWI/vMpiBtDsbbBDUWkDhfekQal9q9hOoR0NWxxTraGypySauw53MWLYWuxTDL54eIgA28iUOZwIdfmQL7NyRfzZAtxsoU+P+8djbMD9B36PRBC/t1s0BpVdAgh5AxhVU4WclhV1ifzYa3/WlQqKjExeiKeHf4sNDINLo6/GDsrduLzPZ9jc/ZmSOQS3D38bn57SxWHzcU5sIjPxUHRn/ymosg4HAhrgFhqhbdRjZjdD0PWGIotFg8MqUacc1NvSOU0H4d0TfsabfiiqBLzy+tgOzyMWi6IMNxPg531VlQ63fi8uIpvrJ/X0QOtWbviT1KjKOQQ0sNR0CFdwnnnnYe1a9ce9z42/K15CBwhnTHkeGQe/Bb4G2wyGx4Y8ACmpUyDyNEAVGQAgb2QHpiO98a/h6LBRVBL1TAoDjcncNmAbV8Dmz4E6vL5TT6xDPt690W5Ppef4PnKQ5Gw/1FIXFrstXtgGByMsdckQaDF80gXw4acLasy47OiSr7gZDO2Vsp1oUZcHuzP12JhTQbW1Dbi5/JaLK0yw+Lx8oUmIxQy3qKYbWxRSta6mBDSs1HQIV3CZ599xucZHc/JmiwQ0hEaGhp4yKmqqoJX7sVvpt/gUXrwf2f9HwaHDAYOLgcW3gU0lgPxE4GJzwBBqQjXhjc9ARtRvPdH4Lf/AuZCfpNX5Y+KAeOQ6c2FW9w0306ePQRRObdA5BMji4WcsyIweFIsNR0gXc680mq8lluGYoerpSJzgckPN4QFYIhefcS/abYmC1vok202j5e3hg6WSXlTAUII6ZZBpwtMNSL/wNHr75B/D/1stb+SwzoGVldXAwrg14Bf4ZA58N7Y9zDYPwVYdA+wrVVHwawVQNZvQPo1wLjHgIYyYNkjQNEWfrfDPwyF6QOR79wNiFYDYsDnEWDcczVMFRPg9vmwx+lF/NREJA8PPb1vPiH/QhXnqaxifFFcxa/7S8WYFhqAaaFGhCpOPb9MKRYQLqZ5aISQbhp0xIcXB2ULQ7JFLgkhp1fzoqvNP2vk5N3VWMt3FnIEpYAlxiWwSq14eeTLGOkWAx+PAGrz4JIIyBs8Ao06FaJLnTDs+gPYOQfY8z/A4+DP5VaokDVoCIqwDyL3erBxam6rFJLcvoituBoylz8aPD5k6+UYdXMf+AWp6K0hHa7S6cL62sZj5szEq+Too1EeUZmpc7lxy748PgyteaHO/0QEQkHDLgkhp0mXDzpsUU3WTpiNg2eLZQoCG7VOCDkd2LpH7GeL/YyxnzVy8u50bLFc1l1NrBBjsXExLFILHh/yOM6vKQN+uRos6+THByMvSADEBwArUKMH/CaOR1JOHTTZ28Bq06UDxiBDVQCfaA9fyd1TGQC/gokIqRkPka/pfSh0eiGMCMV5F8dBTCeGpBPIsNhw2Y5sPpTseKIUMlwY6IeLTH7QSARM252LHJsDKrGAD5IjcZ7J718/ZkJI99bl20s3f+LMWky3Z4FLQkjbsA8PWPt2mYyGh5yIw+Hgi/YWFhZCopDwSk6DrAH/Sf8Pbncr4f3lHuQHKpETpQMUHv4Yd40OorogCNGHIBLYEEERgtVD0eCsgNWdzffxNegRenAGdNXp/LrX50O524catQy9r01CWCLNTyOdQ6bFjst2ZKHK5UYkbwrw1+8Ltw/YUW9t6Z7GsADPTj7C5FLMSotFqoZGZBBC2q5HtZdmJ2AJCQktQ2wIIaf354sqpSfGFgCdO3duU8iRSbDCuIKHnKt7XY3b3Gp4f7kLm6KNsEXw2gxc9UoYDk5GaM3ZEEGApbgQBdGfQ4jIQ7l1I39Or0sMfdZ5CC6aDMEnRaPPC2ekHpr+QUhNNUKlo9BJOmfIYcPTvk+Pg0F65OmFxePB79UNWFhRh9+q63noGaxX83VuTDJphx07IaR76xYVHUII6QhsWN+CBQtQVFQEiVSCVUGrUC4txwWxF+BFTRp8C/+DTTH+sIeL4HWLoNhzHqKrLuXDz3wSEcR+cnir7Py5zNp9KIr8BjKXAdH5N0HqMMDp80B9dhSCxsfQG0w6pYMs5OzM4uva9NYo8b/jhJyjsdCzv9GOdK0KUuqURgj5G3pURYcQQv5NHo8H69evx+rVq/llFnI2BG9AuaQco8NH4zldX/gW3I5Ncf6wh4rgc4sRseNeaGr7wCcCVIOCoJ8YDUErhW1PFap/PgR9Qyr0+17kz88+f/LGqRE9LQ2Cgn5Nk47FWjivrW3glZjW829YjXJTnYVXcnqfoJJzPGqxGIP06jN81IQQQkGHEELapbi4GAsXLkR5eTm/Hh4Tjh8lP6LIXYR+gf3wurYvBFbJifeHPbgp5ETueACq2mTIEw3wuyAG0qC/TvJUaSYoU41o2FSK2qU5bPl3BF/XG/JoPb0zpMM0uj18MU62gCcbctZ6fs3RUjUKHnLYYp6EENKZ0G8lQghpQ7OBjIwM7N69Gzk5ObziwtrZBw8IxqeVn8LsNCPRkIj35YmQLH8IGxOMcAQCPpcEkTsfhKo2CeoRofC78PiLeYrEAnQjwqA9vA7O8fYh5N+cc3Pd7hwU2P+a98qaBpwboEeCWsGvszHv/OdALOBCkx+0Emo/TwjpfCjoEELICbBQs23bNmRmZsLt/mvITnyveGzRb8Gc4jn8erJ/L3yAYEi2vIH1fQLg0fmaQs6Oh6GqS4BmZBj0F8ScMsBQwCHtsaLKjFq3B/FKOWJVcvidhorK6poG3LQ3Fw0eLw83U4P9cZ5Jf8waOIQQ0hVQ0CGEkONgc3BWrFjRct1oNKJ3n96o0FXgncx30FDRAIkgwW29b8INh7agsehbbEgzQqTwwWtVImbXI1A0REEzOgz6804dcghpjyWVdbhhb94Rt/lLxUhQKXiF5coQ/xNWWaqdbohFOCYYfV1chccOFcHjA4bo1fiidwyMMjpNIIR0XdR1jRBCWmHDcX7//XesXbuWX09PT0dy32RstGzE/w7+DwUNBfz2VGMqnkuahoRVr6PItQ8ZCRqIxIC3JgAJex6HxGGAZnQ49OdFU8ghp1WV040xWzJ4Y4BElQINHg9KHa4j9lGLBVwZ7I8bwgMQq5Rjv8WO5VVmvu1qsPF9IhQypGmVvJFAmcOFr0uq+e2XBxnwRq8IyGkBbkJIF++6RkGHEEIOY4sOL1u2DFu2bOHX+wzrg22qbVietxxOb9N8BbVUjZtSpuP68iJ4t32MzHAlKsLlTb9QixIRn3E/BK8c2rMioJsYRSGHnHY3783Doso69FIrsHxgIg8kFrcHuTYHtpgt+LK4Coesjpb9A2USVDj/Gnp5Mg/HBOPeqCD6d0sI6dSovTQhhLQDaxPNuqnt2rWLXw8dGIpny5+F19fUbSrZPxlTk6bifK8Csl8fQb6mGnkDDIC0aSkyeeZoROVfD5FIgN+l8dAMDqHXn5x2CypqechhQ8/eTY5sqbqoJWL01qr4NiMsAGtqG/FpUSVvCc1CjlIQYbS/FucE6DHRqINUJMLeRhv2Ntiwp9GGcocL08MCcFGgH71rhJBugwbfEkJ6PDZcjS38ybqqsbk0fgP98F7Ve/x1mRA5ATf0uga9yzLhWfM+Sj0HkBWvgVeh4r2n7JVqhGRNQ2DDEIhkAvyvSYYyyb/Hv6bk9Kt0uvDowSJ++Z6oIKRp2b/BY7F/w2P8tXzLszlQZHeiv04NlbgpFDUbadDyjRBCuisKOoSQHm/Hjh0tIUfWX4bPqj7jr8lNCVNwW0MDqpdMwQ6tB7VRUkCs4fc5zDJI941Dav3lkAhSCBoJAmb0gSys6X5CTncYfyizCDUuD59Tw4aXtUW0Us43QgjpiSjoEEJ6tMrKSixdupRfdse58UPND/zyg5Fno0/RF1jnLwZi2CfhTZ+GO+pkEGX1R1LVNMgFFb9ZFqmF/9W9IPFrWmOEkNNtfnktX8CTDTl7JzkSMmoUQAghp0RBhxDSY7lcLvzwww/8/x6DBz+7f4ZYEOPZ8PHwr/sJNQFN7XntVUp4iqKgqRiGOM8QqMUKHnDEBin8LoyHIsVIk7fJGePwevFsdgm/PDM6CKkaJb3ahBDSBhR0CCE9Flsnp7y8HGK5GIs1i/m6OG+GjIK4fgEcehF8dgUC/rwdAba+fz1IDIgkbugn9YJ6QDBEbFY4IWfQj+W1KHe6ESKX4o7IQHqtCSGkjSjoEEJ6pIyMjJY20huNG2GX2HG/Phkiy3K4dD6I7GpEb3sMclsY30esF0Ee7w9FLxOUvfwhkh5/MUZCTievz4ePCir45ZvDTTRkjRBC2oGCDiGkx2ELjLEua0x1UDUK5YUYozAgwrcXHp0bIrsWUVsfhcwSBL8pCVAmGyFWSzv6sEkPxNpDszVxtGIB14UaO/pwCCGkS6GgQwjpUWw2G7755hv+f0EvYLVyNUwSOSZJ6wCdA4JNj6htj0JqMcJ0azoUsbSuCOk4Hx6u5lwXGgCthKqIhBDSHhR0CCE9Bms6MG/ePFRUVECmkmGBdgF8Ii8e8Hkh9rNAcGgQtfVxSK16BN45EPIIXUcfMulGcqwOvJFXxhfq7KVW8HVw+mqV/P+644SY7fUWbDJbeKe1myMCOuSYCSGkK6OgQwjpEbxeL+bPn4/8/HzIZDKsD1oPK6y4F3LIQ+sAr4Cw3XdCYtEg6N7BkIVSyCGnR4ndiTfzyjG3rBoeX9NtmRY7FlTUtewz3l+Ht5MjYJJJj6nmXBLkhxC5jN4OQghpJwo6hJAuHV4sFgvUajWEk6wrwhZbXLJkCQ4cOMD32x66HQUowGhBjKgQM9/HdGgq5JURCHloOKSBtFo8ace/Q58PCyvq8FFhBcxuD4JlUgTLpQiSS2H3eDGvrAYOb1PCmWDU4cpgf+TYHNjVYMXuBhsK7U6srKnHOVsP4rPUaPTXq5Fnc2BJZdO/zdsjqNMaIYT8HRR0CCFditvtRl5eHu+axrbGxkaEh4dj8uTJMJlMxw1Dq1atwtatW/n1rYFbkSvKRYogw2SDFSLBB23pYOhzx8N0fRKFHNKugMPCyOt5Zciw2Ftuz7M5j9l3qF6NR2NDMMRPc8x9GRYbbtqbhyyrA5N3ZOH5hDAcsNjhBXCWvxbJtG4OIYT8LSIf+6izk6uvr4der+edknQ6Gk5CSE/EmgewdW/27dsHh8NxzP1isRhjx47F8OHD+WWPx8P3XbNmDaqqqvg+O4w7kKPLwUipEZdLaoAAC2SNoYja/BR0wwPhd0Gr9XIIOYlNdY14/FAR9jU2BRydRMBtEYEY5qdBucOFcqcLpQ4XGtwenG/y44FFJDrxmktsv3szCrD4cBWH7cn+OP+QHoeRBqowEkLI38kGVNEhhHR6ZWVl+O6771BbW8uvK1QKeAI82C3ajXxPPsY0joGyTomVK1di//79SE9Px+bNm1FTU8P390q82KXfxUPODEGLwZJyOAKcELkVCNt5F6TBUgo5pM22my24alc2bF4fNGKBr29za4QJftK//yeVdVRjw9Y+KqzE89klvJqTplVixHEqQIQQQtqGgg4hpFPbuXMnfvnlFz5kTVAK2BeyD/u9+5s+8mYEYInfEqQoU9Cnug9KS0v5xojlYmTqMrFPtQ8iCfC8W4DGvwYOtQsitwyhu2+H2KVF8O2jO/R7JF1HrtWBa/fk8JAzzl+LD1Ki4P8PAk5rrOLzn8hA3oltVkk17ooMPGkViBBCyMlR0CGEdEos2Cxbtqxlbk2VugobjRvh9DmhlCowOiAdZ3vk8C9z4WV1LvaL9iMnKAeT3ZMhb5Sj1FCKP0R/wC24kagOw0PmLDREsvTjg9gSgIhd90BsNiLsybEQSU7cyICQZlVON67anY0alwdpGiWvwKjPwNo2IwxavhFCCPlnKOgQQjrnop7ffoOiwiL44MN+v/3I8MtAvCYUN4qMSCgpQUltKeq1LpTr6jCtMAF7jErMk+TgO8l3gKLpeeRiGe5UJCDNsgv1MU1FIHVFX4TsvQUinxTBdw+ERCfv6G+XdAFWjxfX7c7hjQYiFDLMSYs9IyGHEELI6UNBhxDS6SYYzpo9C1WVVXAKTmwxbYFg9OJ5TxjUrkr4AnJQZGAzGICW2Qv+VehTEIe+daF40VSHBrcVg7UxeLgsAwWqDNRHNM3sNmZfAmPORZDHqhFwXToEJf0KJKfm8flw+/487GiwwiARY27fWATK/1rvhhBCSOdEf+UJIZ1GdXU1Zs2axbuo2MQ2bAvbhhmaSAQ4MiEJy+f7sKqM2KGFsi4Ryrp41jsSlfE/Qh2ZDYfeD+8eMMAdJIOyJB/5iT4IEgcEhxahe2+BsjoVhksSoB4cQnMfSJtYPB7ctb8Ay6vqIRdE+LpPDOJVh0uGhBBCOjUKOoSQTqGkpARz5syB1WpFo6QR+QEHcJdPBrX/Gr7WDXwi6ItHwj/vfEitwXCLPPCEaSFyuqH8MwklfT8A9DWoGdAId7UJ8phG1qcAqso+CNl3E8RKfwTf1xfSIHVHf6ukiyh1ODFtdy72NNogE4nwUUoUBlMXNEII6TIo6BBC/nV2u523jGYVnOYtNzcXTqcTtbJaVBszcUNIMWQaK99fXdkXpoNTAYsRfucmQdvLCEmgCiJBBJ/Xh7L5fojaZEJp2kew+h+AOLgU8Ir5YwwFE6EaFALDRXEQZDSngrTNrgYrpu/ORZnTBaNUgi97R1PIIYSQLoaCDiHkX8PWJ2btopcuXcpDzdEqFBWwGDNxXVgJJAonpJZgBO+fAXlVNGSDAxB8cRpE0iM7pLGwE3J5L5iDtRB+uR81cYtg0x+C6dBUyKxRMF6dDFWaid5l0uZ/o4sqzbjnQAFsXi8SVQrMTotBlJKaVhBCSFdDQYcQ8q9gQ9LYejhsQU+GrWQs18tRI9Qg05GJEpQgVuLENeEVEMs8UJhjELbtHoiUPoQ9NQoS7clPNPUjwyDzVwCzxRB8IohClAi6vTck7DbSI7m8PjR4PFCLBT707GRr0rCGA0sqzfiwoII3HWDYOjmfpEZDR93VCCGkS6KgQwg549iwtJ9++ol3VBMEAfI4MTY0/owKWw28IsArAOe5/TA0rgyC2AdVdQpCtv8HurOj4D8upc1fR5liRMh9A+EsaoCqrwkiMa2P05OHnrF20BVON78uEQFqsRgascDbQ8ep5IhTKfj/i+1OfFJYiXx7U5WRNR24KdyER2NCIBFowU5CCOmqRD5Wp+/k2MmRXq/nnZjYp8CEkK5jw4YN+PXXX/llvUEPc8MGDNinBaJ9EPnXQeRfA7GhDlK/Rr6PpmwgAnffCNOMAdAkGTv46ElXtL/Rhkt3ZKHO7WnX41jr6OvDAnBDeABMMmofTQghXT0bUEWHEHLGFBQUYMWKFfxyZFIEGld+jfTkEChvPNxJ7Sh+BWfBL/NqhNzaH4oYPb0zpN0OWuyYsjObh5wBOhW+SYuFIBLxNtEWjxdmlwd5Ngey2WZ1IMfqgBc+XB1ixJUh/rzqQwghpHugoEMIOSMcDgcfrsaKxoYQFfRLv0bQRXYojFv5/craBMgbIiGzhEBmCYW8MRQejx7hd/SDPFxL7wppt1yrA1N2ZqHa5UaaRolv02Khlzb9mWs9z2aAnlqME0JIT0BBhxByRixfvhy1tbUQS9yIL14M+XUlEIl9EDu1CNo/HbLK/nDIBYi0Csj85dD0UkE/PAzSACW9I6TdCmwOXL4zC+VON5LVCsxLj2sJOYQQQnom+itACDntDh48iO3bt7Nmvegb9DsUceX8dk1FPxj3TYO9bzxi70mGSEwTvck/4/B68XlRFd7KK0ODx4sElRzfp8fBn0IOIYT0eBR0CCGnlcViwYIFC/jlaOlOqFnI8YoRvP96OKqHIejWAdCGU1MR8s+wIZHLqsx4JrsEebambmlpWiVm9YmlRgKEEEI4CjqEkL/F4/Fg9+7dvPOJRqOBVqvl25o1a3jYUQv1CE3P4vv651wAhF6Cfnf0oioOOWWFpsblRrBMetx1byxuD36vacBXxVVYX9fUqS9QJsGjsSGYGuwP8UnWyiGEENKzUNAhhLRbTU0N5s+fj6KiouPez841k4xbIVbaIWsMha3hPPS7JZle6R5WcfH40OZ1aPJtDnxdXI25pdWodXsQIJVgkF6NgXo1755WYHdiSWUdVtU0wO71tax3c1tEIO6KDISGFvUkhBByFAo6hJB2nbzu2LEDy5Ytg9PphFwuR69evWC1WtHQ0MA3u92ORPNmqEcVAz4RDHuvQa97x9Gr3EMU2Z34vqyGb1VON56OD8W1IcbjVmfYv6c/ahrwRXEVVlbXo3XD8SqXG0urzHw7WpRChvNNeswIC0CkUn6GvyNCCCFdFQUdQkibsOFoixYtQkZGBr8eFRWF4f2T4a0qhn9oBLQqAwSpFJtfuAf2y0r5Pn6FZ0E1/BxIVLT4YnfAgkmm1Y5fq+pR7/ZAKxZDIxGglYjh8vrwc0Ut1tU2HhFYHswswoqqerzRK6Jl7kxzwHk5txS7G2wt+441aDEjPAAjDRrsb7TjT7MFW80W7Giwwk8ixnkmPS4w+SFFrThucCKEEEJao6BDCDklr9eLuXPn8qFqgiBg9JhRqF01C7b/ewWC2ofKehGqGwCRTwTrtADIdBZI7P4QFV6ImOnR9Ap39SpegxVLKs18y7E5TvmYkX4aXBHizys6L+WU4tfqeozbkok3e0Xw9WzYbZvNFr6vWizgmhAjpocZEadStDwHG7bGNkIIIeTvoqBDCDml/fv385Ajk8kwfHxfVL9+Pwy9garn3BAJTZ/f+7yAxy6HVNFUzTHtvxaxt46nT967sFqXGzftzWuZ9N88L2a0QYsYpRyNHg8a3F7+f7vXi5F+WkwJNhwxnGyMvxb/2Z+PDIsd0/bkHvE814cF4K7IIATI6E8RIYSQ04/+uhBCTsrtdmPlypX8slxTD99HM6G5RoBP5wQbPCRyqOGTWSASAImq6dN+belQCBFnQRFIn8h3VblWB67ZncMrOEpBhLMD9HxezHh/Xbsm/qdolFg2IBEv5Zbik8JKSETA1SFG3BcdhBC57Ix+D4QQQno2CjqEkJPaunUramtrIZeZ0Ve8At5pbv6LQ2INQFDGddBU9YVP5IFH2gC33AyvxAZ3YySSnk6lV7aL2lzXiBl7c1Hj8iBcIcWctFj0Uiv/9vMpxAKeiQ/DVSH+fF5PmIICDiGEkDNPaO8D2BoZF110EUJDQ/mQlJ9//vmUj1m1ahX69+/POzTFx8fjq6+++rvHSwj5F7EOaqtXr2YD05CW8DtEyTbAK/B1cWI2vABtwFgE3NAbxqkpME7oB2P6cPjFjkbCHSMhkrT71wvpBOaX12LKzmwectK1Kizpn/iPQk5r7Hko5BBCCOm0FR3Wealv37644YYbcOmll55y/9zcXFxwwQW47bbb8M033/AhMDfddBNCQkJwzjnn/N3jJoT8C9atWwebzYYgvywojI0Q3ApEbnkCIlcETNckQ5lqpPehmzC73HghpxSzSqr59fMD9Hg/JQoqMQVWQgghPSTonHfeeXxrq48//hgxMTF44403+PXk5GR+8vTWW29R0CGkEzObzdi0aROv5kRF7OW3GfLPhjqxHwInxUNQ0MjX7tJV7ZdKM544VIRyp5vfdnuECU/EhUJMLZwJIYR0YWf8TGXjxo2YMGHCEbexSs699957wsc4HA6+Nauvrz+jx0gIOdYff/zBGxGEazMgNzRCcCnhKx2H4Jt70cvVjRb3fPRgEVZUN/2OjVPK8WpSOEYYtB19aIQQQsg/dsbHJJSVlSEoKOiI29h1Fl7YkJjjeemll6DX61u2iIiIM32YhJBWSktLsXPnTraCDsJimhYINRScjYRrRtPr1E1sqG3EuC0ZPORIRSLcFxWElYOSKOQQQgjpNjrl4OtHH32UD5tp3goLCzv6kAjpMRobG/Hdd9/xy9HKvZD5sWqOCt7y8dDE+XX04ZHTYFmlGVftzkaDx4v+OhV+G5SEh2NDeHc0QgghpLs440PXgoODUV5efsRt7LpOp4NSefxOPqw7G9sIIf8ul8uFefPmoa6uDnq5FMEJWfx2Q/45SLpuFL0d3cB3pTWYmVkAjw84N0CHj1OiKeAQQgjpls74x3fDhg1rWWyw2YoVK/jthJDOw+v1YsGCBSgqKoJCoUBSzWJI/SwQXGp4q8ZDGaXr6EMk/9DHBRW4J6Mp5FwR7I/PUmMo5BBCCOm2hL8zrIWN3W8av9/UPppdLigoaBl2Nm3atJb9WVvpnJwcPPTQQ8jIyMCHH36I77//Hvfdd9/p/D4IIf8QW+9q7969EAQB52iUEA9r4Lf7552DlGkj6fXtwurdHjxysAj/zS7h12+NMOGtXhGQCKKOPjRCCCGk8wxdY6ukjxs3ruX6zJkz+f+nT5/OFwJlk5ibQw/DWksvXryYB5t33nkH4eHh+Oyzz6i1NCGdqJLDPqxgiwEzZ6emonb9s5CkWCE41fDUjYcinLpwdUVenw/zymrwYnYpqlxNraMfiw3BXZGBfMFnQgghpDsT+dgiCp0c69DGuq+xxgRsbg8h5O9jP/JVVVW8Gsu2vLy8lg6Iw9PTofnpKVivs0AkAKYDVyPpsochD9bQS97FbDVb8PihIuxqaHpv41VyPBcfhnFG+h1KCCGka2trNqAV/wjpYSGHzcNpHnraTCaToV+fPjB8/zzqrrVBEAB90WhIvJMo5HRB/1dYgaeymoapacQCHogOxg3hAZCxN5YQQgjpISjoENKD7Nu3j4ccNmwpOjqaDy1lW0hICDJnXoqiqWZIpF6oqnpDmzsNCQ/16+hDJu30bUl1S8iZEmzAk7GhCJRL6XUkhBDS41DQIaSHsFgsWLJkCb88evToI+ba5b71IApH5kCqdkHWEA7/Pbcj7v7hEJT0K6IrWVhRhwcym9Yduz3ChKfiQmkuDiGEkB6LzmII6SGWLl0Kq9WKwMBAjBrVtCaOJW8fdr12E+oHN0IaYIPE7oeg7fcg5o5RkPjRWlZdycrqetyxPx9eANeGGCnkEEII6fEo6BDSAxw4cIC3jmZD1iZPnoyMvFXY/93j0AXaIb2sEWxgk8itQMj2exF+7VhIg9UdfcikHTbVNeKmvblw+XyYHOiHV5LCqZJDCCGkx6OgQ0g3x6o4rMU7M2LECCxaOgMR6nIYhzU27eATQVs+CMbsyfA/dwxUCf4de8CkXXKsDly3Owc2rw8TjDq8nxwFMbWOJoQQQijoENJd1NbWYsuWLXA4HDAajQgICOD/Z+vjsIV+2XWfYy1iQwsgVrgg8kihLxkFQ9658ImCETwpAer0wI7+Nkg72D1e3LwvFw0eLwbr1fg0NRpSWgSUEEII4aiiQ0gXV1FRgXXr1mHPnj28ffSJjBszBDmb74Iq1gV5fSTCtz8Aj96EkMtioUgxQkQnyF3OU1nF2Ndoh1Eqwf+lRkMppvbRhBBCSDMKOoR0UeXl5fjjjz+QkZHRcltcXBzCwsJQXV3NFwVl/3e73Rg5ciQOzrsfuvGVgFcMv73TEHLjKMijaPHIrurn8lrMKqmGCMAHKZEIphbShBBCyBEo6BDSxbDgsnbtWr55vazHFpCcnMw7qYWGhh6xL7vfbrdjxwf3wjuikt9mzL0QoZPOp5DTxefl3H+4jfQ9UUEY60+BlRBCCDkaBR1CupCSkhL8/PPPfLgak5SUhPHjx/OW0ccjCAIsOzahTrsbCoUT8oYICPZL4J8W9C8fOTnd83IsHi+G6tV4IDqYXlxCCCHkOCjoENJFqjirVq3C+vXr+TwclUqF888/H6mpqUe0EXZkZ8ORkwNPXR3fvGYzdmXPh2IKG7Im8CFrKfcN7dDvhfw97H3fYrbg9byylnk5H6dGQ0JzqwghhJDjoqBDSCfncrnw/fff49ChQ/x67969cd5550Gtblrrxut0ouHn71H44zcoiXLDY3CzMWsQ+bwQfF7gwlq+nzH3AoSefz4EFVs1h3QVLq8Pv1TW4ePCCuxqsPHbJCKal0MIIYScCgUdQjp5yPnuu++QlZUFiUSCSy+9FCkpKfw+R2kZDr39CQrqC+HqVwjVTTkQi70QH+d5ZA3hkNgug38/GubUlfxWXY+HMwtR7HDx63JBhClB/rglwoREtaKjD48QQgjp1CjoENJJOZ1OzJs3Dzk5OZBKpbj66qsRExPD7ytfuRarfl8AzZBVUPjVQn74MQpzDJS1SYev+QCRD/AJUBaOQuIDNGStMw1DY9WZCqcLow1aKI5qC+32+vBaXhneyS/n1wOkEtwQHoBpoQEIkNGvbUIIIaQt6C8mIZ005MydOxe5ubk85FxzzTWIjo7m9+3+aA4O2P9AwPhV/LrILYOubBj8CsdB1hAFmJQAm7dxeO6OIBFgvDoaYjUNWetolU4XfiirxbyyGmRa7Py2QJkEN4ebMC3UCL1Uwve5bV8+1tc18vtvCAvAU3Ghx4QhQgghhJwcBR1COmHjgW+//RZ5eXmQyWS49tprERkZCY/Hi0WPvQtv0h8ISNrL9zXknQNj9sWAoIJ+WCg0w8Mg8Wuu75DOoszhwqMHi/BrtRmew2u6KgQRdBIxKpxuvJBTirfzyzEl2B9LK+tQ7nRDJRbwZlIELg4ydPThE0IIIV0SBR1COpnly5fzkCOXy3nIiYiIQFVlNRY89y0Ch/8AbWAp76AWdGA6tNXj4H9+NNQDgyDI6ce5M/L4fLhlXx7vmMb016lwZbA/Jgf6QSUW4+eKWnxQUIEMix1fFVfxfRJVCnzWO5rm4RBCCCH/AJ0ZEdKJ7N69G3/++Se/fNlll/GQU5aXg1/eXoiw8V9Cpm2E4FIhdNedUKIfQu9Jg8RAk9I7MxZiWMjRiAXM7xePNK3qiPtZFefyIANW1jTgi6JKhCtkeDouFGrJ8dpKEEIIIaStKOgQ0kmwRUAXLVrEL48ePRqJiYko2P0nVn6+BuETv4BUZYfUEojwHfdBEZCI4OtTqVV0J7e7wYpXc0v55RcSwo8JOc3YWkgTjDq+EUIIIeT0oKBDSCfgcDh4G2nWTjo2NhZjx47Fod9/wcaf9iPk3K8gVTogr49A+LYHoU6KQcAVvSCS0uT0zszm8eKO/flw+4ALTHpMDaa5NoQQQsi/iYIOIZ2g1fDChQtRXV0NrVbLh6xlfPt/2L6pFkHnz4JE4YS8PgoR2x6Ebkgi/M6PhYh1VSOd2vPZJThkdfCuaq8mRvCqDSGEEEL+PRR0COlg69atw759+yAIAqZOnYrCBd9g58YamC6Y3RRyzNGI2PYA/MalQHdWJJ0wdwGraurx+eHGAm/3ioSR1r4hhBBC/nUUdAjpIB6PB0uXLsXWrVv59bPPPhu+Azuwc3UJjJPnQaJw8QVAw7c9AP9z+kA7Kozeqy7goMWOew4U8MszwgJwFs27IYQQQjoEBR1COoDFYsH333+P/Px8fn38+PGIFTxYNWsz9FMWtYScsG0PwHhRX2iGhND71AWsq23ADXtzUe/28hbRT8aFdvQhEUIIIT0WBR1C/mXl5eWYO3cu6urq+IKgbE5OGDz47dHPobpyHWRaC6SWIIRuvxemy/pD3S+Q3qMu4NvSajyUWcibDwzSqfFlnxi+6CchhBBCOgYFHUL+JU6nE1u2bMGaNWv4ZYPBgKuuugo6hx0rb3sBuDIHyoAqiJ1ahG+ficCLB1HI6QK8Ph9ezinFuwUV/PolgX54q1ckFBRyCCGEkA5FQYeQM8ztdmPbtm084LAha0xMTAymTJkCcU01Vt36NGyTG2EIy4HII0PYjnvgP2IQ1AOC6L3p5PY2WPFsdgnW1Dby6/dFBeGhmGBqGEEIIYR0AhR0CDmDbaN37tyJP/74A/X19fw2Pz8/vkZOWloa6g/l4tcnvoPvfDECErYDPhFCdt8Kfdww6MdH0vvSieXbHHgltwzzy2v5daBDjuMAAC+WSURBVKlIhDd6RWBqsH9HHxohhBBCDqOgQ8gZ6qi2ZMkSXslh2Po4Y8aMQXp6OiQSCbL+2I01c3fCf9JaaMMO8n0CM66BVj0GpssSqSLQybi9PuTbHThkcWBVbQO+KamGy+fj910c6IeHY0IQo5J39GESQgghpBUKOoScZjabDf/73/+Qk5PDr48bNw7Dhw+HVCqFw+bGmi82IicrE6Hnvwu5zgx4JAg+MB1qx1kIvbsPRDS3o1NU47bVW/FNaTV21FuRY3XAeTjYNBtr0OKxuBCkaVUddpyEEEIIOTEKOoScRrW1tfjmm29QVVXFgw3rqNarVy9UFzdiz6psZGwship0I6ImzIIg8UBiDUDYrjshlyYh9La+EOT0I9mRGtwe/Fhei1nFVdhvsR9xn1IQIUGlQKJagStD/DHSoO2w4ySEEELIqdFZFSGn4dN/FnDYmjgrVqyA1WrlQ9WuuvIqWMsk+PG1rSjLrocy4BBChv0ETcgh/jhVVW+E7rkNysRIBEztBUEupveig+xpsGJWSTUPOVaPtyXYTA404AKTHklqBcIVMggiEb1HhBBCSBdBQYeQv8FsNmP//v0oKCjgW3M3NSY4OBgj08/B6i8KUVNsgTLgICJGz4c6OLtpB68AY+6F8M+eDMN5cdCMDqc5OR2ABZoFFax6U40dDdaW2xNUckwPC8DlQQb4SelXJCGEENJV0V9xQtrI5XIhMzMTO3bsQHb24dBymCAICA0NhVEXAleOEau/yoVUU46wMV9AG5TTEnB0xaNgzLsQEk8gAm9MhSLBQK9/B1TgvimtwXPZJTC7PS1d01jlZlpoAIb5qSl4EkIIId0ABR1C2rAODmsRzTqo2e1/zdsICgiFvzoUcpcOnnol6g86UFLvBESN0CUtRVDvhRCLvfB5BGiLhyMw72JI7QFQ9PKH3+Q4SAwKeu3/ZdVON+7PLMCyqqZ235EKGa4LNfI5NyaZlN4PQgghpBuhoEPISTgcDnz//fctFRydToeo4ATUZ6ph3QtU8VvdbBp70w+UrhD+Q9+Hv18Nvy6tjEd4xq2Q2UyQmJTwuyoWiiRaa6Uj/FFdj3syClDhdPMKziOxIbgtwgQxzbshhBBCuiUKOoScAJt38+2336K4uJh3UBszdCLKtopQtqYp1NhlNTCH/AnBLx9yXTm0qlrEaBvAukN7nRIEHrwK/iVnQZAK0F0QA83wEGod3U51LjdUYgEyQThm+Fmjx4typwt1Lg/iVfLjzqfxHm4T/V1pDeaUVrfMwfkwJQp9qC00IYQQ0q1R0CHkOOrq6jB79mxUV1dDoVAiTj0Uu/7XyO/zyGshDPw/9A7JguTI8+8mpdGIP3g3pA5/SMNVCLiuN8R6WkyyvQHn5n15WFvb2NIBTScRQy+RwO71otLpgs3717o2rBdaL7UCg/VqDPXT8H2XV5n5Vu5kFbcmM8IC8GRcKA9PhBBCCOneKOgQcpSKigoechoaGqDV6GC0pKEizwOvyAlH39noFbMFSmlTC2K3XYCjTg63WQuhIQCRzpEwNQ6DyAdoxoRCf04sRGJqSdwexXYnrt6dg8xW69iwUGNzuo8ILYxWLEAjEaPU4cIBi51vX5dUH7PPBKMO14Qaae0bQgghpAehoENIK06nE3PnzuUhx+BnhKo0GRazAFHINgQO/AIBSiffz1WngWbvBQivGQCVzAiR0GoNHLEbxul9oUykuTjHw4ad/VJphhc+TPDXQS3567XLsNhw1a4cHlxC5FLMSYtFuFyKOreHd0gzuzyQCyIEyaUIkEmgFjc9llV4NtdZsMVswSZzI2pdHoz11+K8AD1GGjTHDH0jhBBCSPdHQYeQVlatWsUX/1QpNZDl9oLTIYI89XtEp6wAm7PusUuhOXg2EssvhcgnBloap3kgqH2QRfnBcHEKxDoaqnY8Tq8XD2UWYV5ZU7MGhSDCeKMOkwL9oJeIceu+fB5o2DyaeX3jEKaQ8f30p1jPhnVMuzDQj2+EEEIIIRR0CGmlpKQEGzdu5JelJVHwOn3QDn0XYZF7m27LT0d89k0QuzVsmjvk8VJoRiVAGqSBWCeDSKAhaqdq7Xzj3lxsMlvA6ivhChkK7E4srjTzrRmbZ/N1nxgYaLFOQgghhPwDVNEhhNVjPB4sXLiQD6tSOAKh8CrhP/ZZBJhK4fMBgRlXwVB4Nq/qqAYFQjc+GhI/qtq0FZtvM213DvLtTj5n5pPUaIzz12Jvow0LK+r4xu5jQ81YRzQlNQsghBBCyD9EQYcQgFdyysrKIBFk0Dv9EHHWf6HU1wJuKcL3/Aeayn5Q9jVBf040JP600GdbWdweLKiow9NZxWjweBGlkGFWWiyS1E2vIWvxzLbHYkNa5uWIaF0bQgghhJwGFHRIj8daSLO5OYyyNgKRI9+FQl8Lwa5DxI6ZkDdGw++SeKgHB9NJeBt4fD6sr23E92U1fEiazdvUoW6oXo3Pe8fAKDv21w4LN6GH5+MQQgghhJwOFHRIj8aGqi1atAhutxsqkRHBYTuhMFRCcKoRvflpSKVBMN2eCnmkrqMPtdPJtznwcWElcqwOOLxe2L0+/v9KpxtVrr/aQMcoZbgqxIhbI0yQU/czQgghhPxLKOiQHl3J+fXXX5GXlwexIIbGbIRpyPv8PtOhqVCGRMN0bQrEGqo0tFbmcOGtvDJ8W1oDF5vAdBysg9rkQD9MDfbHAJ2KKmGEEEII+ddR0CE9js1mw+rVq7FlyxZ4vV5+Eq6xxCE49TuIZW4o6uIgqx2GoMfTIKJJ8S1qXW68m1+OL4urePWGGWPQ4tIgA1Riga9voxAEfrm3RgkFvXaEEEII6UAUdEiP6qy2detWPh+HhR0mISEB/u4ElGZthj56L+ATIXD/tYi6cQiFnFb2Ndp417Rih4tfH6RT45HYYIwwaDvmzSSEEEIIOQUKOqRHKCoqwi+//MI7qzGBgYEYOWQMXJVqbFqYiZgJX/Hb/QrPgiI6DbIwtlYOYZZVmvGfA/mwerx8vs1zCeEY76+l4WiEEEII6dQo6JBujVVuVq5cySs5jFyuQGJIOlxFflj7aTm/zT9pMeR+dRA7dNAeOh9hT/Xp4KPuPI0a3i+owIs5pWAD1UYZNPg0NRp+tJAnIYQQQroACjqk2zpw4ACv4lgsFn49UBMNb04wyvJZcwErRGInXLG/IiBlKb/fdPAKhEwdBkEuRk/X6PbgsUNF+L6sll+fHmrE8wnhkAqijj40QgghhJA2oaBDuu0CoMuXL+eX1TIdZBUx8JXpIRK5EZK8BY2Bi2DyL4dM2jSpXlmbCImlP7R9g9CT1bjc+LyoEp8XVaHO7YEA4LmEMNwYburoQyOEEEIIaRcKOqRbYV3UWMvoTZs28esqexiUZTEQiYCI1OWQR/8MidoN/eH9BZse+vKh0GefjaiHxqC7K7Q78WZeGbaaLYhUyBGvliNRpUCUUobfqusxq6Saz8Vh4pRyvJgYjjH+1HCAEEIIIV0PBR3SbbhcLsyfP58PWWPU9TFQWsMQFLwBfsnzIJgc/HbBoYGuYjC0pUOgrEuADz74X54IiV6O7lypeYe1hi6qgvPw2jeHrA6srDl2X9Ya+u6oIFxg0kPMEiIhhBBCSBdEQYd0C1arFXPnzkVhYSEAEbR1SdBLHYgY/DAk0XV8H8GlhDFnEvwKJgCCGOrUQKguNEGeaIAg657zcpxeLz4urMR7+eVoOFypGeGnwY3hAahyupFldeCQ1Y5sqwORChn+ExmIcdRRjRBCCCHdAAUd0uU1NDRg9uzZqKiogMgrhq4uBSEh6xEwYCFEEh/gEcNQOAHGnIsg8jPAdF0CFIkGiCRsBkr3xYag3bg3F3/UNPDrqRoFnogNxVgKMoQQQgjpASjokC6ttrYWs2bN4v8XPDL4N8YiMv0jqKNz+f3qyjQEZlwHkccfxgsToR4UApFY1CO6pk3bk4sNdY1QCgJeSQrH5UEGCDQUjRBCCCE9BAUd0mWxCg6r5LCKjuCWI0RQIXLsc5Bo7YBXDNPBqdAXTIBscACCzk2CoJKiJzC73Lh6dw621VuhEQv4Ji0WQ/xoAVRCCCGE9CwUdEinV11djYMHD0Iul0OhUPCNNR74+eef+YKgErcKYQorIoZ+BpEASC1BCN19O1zuIJhu7wtVlAE9RbXTjSt3ZWNPow1+EjHm9o1DP52qow+LEEIIIeRfR0GHdGosyHz99deor68/7v0yjw5Gpx/CR73MQ462dCiC909HbbQIfa4bB0HR/f+Ju7w+bK23YGV1PX6uqEWR3QWjVIL/pcchRaPs6MMjhBBCCOkQ3f8skHRpixcv5iFHqVDD5B8It88Fl9sBu80OX4MayroQRJ71FASpB6rqVBj2ToflHAPSxgyEqBvOR3F4vci3OZFrcyDH6sCOBitW1dSj3t3UUY0JkUvxfd84JKgVHXqshBBCCCEdiYIO6bR2796NvXv38nbR8uIE2PJ0/HZBEEEnF8NpcyJ86MuQ6RshsRlh3H0DJDdEISEhCd3NlrpGPJBZhCyrHX9Fmr/4S8UY56/DeKMOE406aCXds102IYQQQkhbUdAhnVJdXR2v5jCqhkioxQYo/GRorLbD6/XBaXMjIPF/0ETmQuSRIGzXncga5Ma53TDkHLTYcd2eXJjdHn6dNRiIVcoRo5IjQaXg7aLZPBxa3JMQQggh5C8UdEin4/V6eaMBh8MBqUsLPRRIG7ECJrUYIpccbpsCVVlbYem7me8fdGAadiqsuOTCa9DdlDtcuHp3Ng85A3UqfN47BoEySbcclkcIIYQQcjpR0CGdzqZNm5CXlwcBYhgcQYge9xwaVQ40sjvZtBMtgEA2oA3QF45FTVUs+t89GGKhew3Xsrg9uG53Dm8uwCo4X/eJhVFGP7KEEEIIIW1BZ02kUykrK8PKlSv5ZU1jGCKHvguJygGRRQ9Y9PBJbfDJbBDJnFCZE6HNvBQHL6zGqIA4dCdurw8378vD7kYb76D2bV8KOYQQQggh7UFBh3QaVqsV3333HTweDxQuf8T0+Q5K/2qIHTpEbXsSUnvAMY/5utdSPDz0WXS3dtEPZhbi95oGKAURZveJQbRS3tGHRQghhBDSpVDQIZ1mXs78+fNRW1sLuViFaNOf0EVkNTUa2Hk3tkqKsS9wDVReBVQeBZReBQ6q83HT5PsgEbrPP+O9DVbck1GAfY12CAA+SolGf726ow+LEEIIIaTLYedS7fbBBx8gOjqar1A/ZMgQbNmy5YT7fvXVV3zidOuNPY6Q1latWoWsrCyIBQlC5eUwJW/gtwfvuwnZDi8+S1gI+yA57COUsJ+lhv08DS6fOgMJhoRu8UI6vV68mluKc7cd5CHHIBHjk9RonGvSd/ShEUIIIYR0Se3+KJwNLZo5cyY+/vhjHnLefvttnHPOOcjMzERgYOBxH6PT6fj9zahjFGktIyMDa9as4ZeDxB6E9V/ALxuzLoa1IhovJ76HTyZ+iiT/7tc62ufzYX1dI548VIwDFju/7QKTHi8nhsMkk3b04RFCCCGE9Jyg8+abb+Lmm2/GjBkz+HUWeNh6J1988QUeeeSR4z6GBZvg4OB/frSk26mqquJD1hiTXI7o9K8gEnzQlg6FMuss3JnwGmaOeqDbhZxalxv/K6vBrJJqZFkdLYt+vpwYgUmBfh19eIQQQgghPSvoOJ1ObNu2DY8++mjLbYIgYMKECdi4ceMJH9fY2IioqCg+D6N///548cUXkZqaesL92fopbGtWX1/fnsMkXYTFYsG8efP4vyudXI7Y1G8hlruhrE2Eac91eDDmfYzpMwGT4iahu8iy2vFOfjkWVdTB7vXx29RiAZcHGfBgTAgCqH00IYQQQsi/H3TYp++sI1ZQUNARt7PrbPjR8SQlJfFqT1paGsxmM15//XUMHz4c+/btQ3h4+HEf89JLL+GZZ55pz6GRLthhbfbs2fzflEIqQUL8T5BprJBaghG64068bZoDeZQeDw96GN1BhcOF1/PK8E1pNTxN+QapGgWmhQbgsiADNJLutQYQIYQQQkhHO+PtqoYNG8a3ZizkJCcn45NPPsFzzz133MewihGbB9S6ohMREXGmD5X8S2w2Gw85bM0cuVSGpMjfoDKyNtJahG+fiV+EX7EjvBDfj/keUrG0yy/6+WFhBT4qrITV4+W3TTTqcE9UEAboVDRfjRBCCCGkMwSdgIAAiMVilJeXH3E7u97WOThSqRT9+vXjHbZORC6X8410P3a7nYec0tJSyKVyxJq2QheWD5FHhrCd9yKzbj8+S1+NT8d8iiD1kZXDrljFOX/7QRTZXfx6ulaFp+JCMdyg6ehDI4QQQgjp9trVXlomk2HAgAEtK9czbN4Nu966anMybOjbnj17EBIS0v6jJV0+5MyZMwclJSWQSaWIC9yGgPidgE+EkN23wlJowaNp/8N9A+/DoOBB6Mq8Ph/uOlDAQ06YXIr/S43G0gEJFHIIIYQQQjrr0DU2pGz69OkYOHAgBg8ezNtLs0nlzV3Ypk2bhrCwMD7Phnn22WcxdOhQxMfHo66uDq+99hry8/Nx0003nf7vhnRqCxYsQFFREWRSCRJDN8IQdYCHnKD910OWE4D/pL6F8TETMS1lGrq6DwoqsLq2AUpBhG/7xiFJTWtHEUIIIYR06qBzxRVXoLKyEk899RSfY5Geno5ly5a1NCgoKCjgndiasZXuWTtqtq/BYOAVoQ0bNiAlJeX0fiekU2Pv/4EDByASedErcjX0YTmAV0DInluhzo3H0yGvQhcWjmdHPNvl561sM1vwSm4pv/x8QjiFHEIIIYSQDiDysRULOznWjECv1/OubWzxUdL1/PDDD9i/fwd691oNfUApRB4pQnfdAWWOCe9r38favm58e8G3iNXHoiszu9yYsPUgCu1OTA70w8cpUV0+uBFCCCGEdMVscMa7rhFSXV3N24n3Sl7XFHLccoTvuBfSLAHPhbyOzXEOvDXirS4fcthnBg9kFvGQE6mQ4bWkCAo5hBBCCCEdhIIOOePWrVsHra4MAQFFfLhaxLYHIc6ow8NJs3AgzI3b+t6GCVETuvQ74fB68V5+BRZV1kEiAj5OjYKO1sYhhBBCCOkwFHTIGcVKirt27UTvPjv4dX3xaAh78jFz4C/INrpxSfwl+E/f/3TZd8Hp9WJeaQ3eyS9HsaOpjfQjMSHor1N39KERQgghhPRoFHTIGcUaT/j5FUKvr+TzcjS7BuD+UXOQrXJgRNgIPDnsyU4/vIsNSVtb28iHpElEIl6xkQgiVDrd+LiwomWdnGCZFPdGB2F6qLGjD5kQQgghpMejoEPOGNZ2fNvWrUjr21TNMRRMxG+K9chWNSDFmII3x7wJqSDt1AFnVU0DXs4txa4G2wn3C5JJcFdUEK4NMUIhbtfSVIQQQggh5AyhoEPOmE2bNsFgPAS1pg6CSwnxvkR8kvYJwjTh+GD8B1BJVZ321d9U14iXc0qxyWzh11ViAcP0Gnjhg8fng8vngwginBegx7WhRigp4BBCCCGEdCoUdMgZYbVasWXzRvTpu4tf98+7AG9ELIdELMWHEz5EgDKgU77yjW4P7s8sxIKKOn5dLohwfWgA7owKhEnWeatPhBBCCCHkSBR0yGnva75582Zs27YN/sZ9UCobIXbo0Jgbgu3JS3BlwpWdto10ttWOGXvycNBq5/Nwrg4x4r7oIITIZR19aIQQQgghpJ0o6JDToqKigreR3rt3L7xeLyQSByIjd/P7jNmT8UjMYsjFctySdkunfMV/rTLjjv35aPB4eVOBz3pHY6CeOqcRQgghhHRVFHTIP1ZZWYn/+7//Y+0HEGAqQqB/EfTGIgiCF1KrCQVVGhTElOH6XtfDpDJ1qlfc4vHgg4IKvJlXzq8P0avxaWo0AuU0TI0QQgghpCujoEP+cWeyRYt+RmzcaphMuRAEX8t9ssYwBO6dhmfD5kAlUWFG7xmd4tXOsTqwsrqebxvqGuH0NR3zDWEB+G98KGQCdU4jhBBCCOnqKOiQf2Tnzp3w+pYjKCin6R9UfTD0FcOgLR8EuSUUvxp/R7msGrek3AJ/hX+HvtplDheu35OLnQ3WI26PUMjwcEwwLg/u2OMjhBBCCCGnDwUd8o/WyVm16jv07tPUWS147w3Ql4wG4ITSvwh7U3firfofoJVpMT11eoe+0sV2Jy7fmYVcmxNSkQhD/dQY76/DeKMO8Sp5p1+0lBBCCCGEtA8FHfK3LV++FFFRq/lcHFVVKrRFyfAb6YRq3FB41DK8+vPFgAiYkToDOpmuw17pApsDl+/MRoHdyas3P6THIUop77DjIYQQQgghZx4FHfK3ZGdno6JiPhISKwCPDIH7roVneiQWi7OxeduT2FK2BWaHmQ9Xuyb5mg57lfNsDly2IwvFDheilTL8mB6PMAW1iyaEEEII6e4o6JB2c7lcWLpkLhKStvPrpkOXYZN0D17e+twR+2mkGjw59EmopKoOeZWzrHZM2ZmNUoeLD0/7X3ocrYlDCCGEENJDUNAhbcbWxykoKMCGDethCloBicQFeV0sxIeS8Grf1yEVpEgPTMeQ4CEYEjIEqQGp/LaO6AQ3r6wGTxwqhsXjRaJKwYerUctoQgghhJCeg4IOOaWamhrs2LkNBw6shddTAp2+AhHBRYBXjKC90/FE9CxIJVLMOX8Oevn36tBXtNrpxoOZhVhSZW5ZF4ct/mmS0bo4hBBCCCE9CQUdclLr1/yIytqXoVbXIjn5rzVyGGPuhdhRtwv7o0rx/NDnOzzk/FZdj/syClDpdPPOag/FBOM/kYEQU0c1QgghhJAeh4IOOaHKylLUmF+AVttUHfG5JZBYTFBYw6A2J0GWmYSX+76KKYlTMDl+coe+kl8VV+GRg0X8coJKjg9TotBH2zFzgwghhBBCSMejoEOOy+Px4NdldyA4zAy3U4W4LU9Cbg2CCELT/Q4z3gh4H0mBKXhk8CMd+ir+WmXGY4dDzvRQI/4bHwaluOk4CSGEEEJIz0RBpwex2+1wOp28qQCbsM82uVwOtVp9zL5LF3+IoNCmhUAD909DTWUuKmRbUaisRqa6FH8aiiH46fD92C8hE3dcu+ad9Vbcui8fXgDXhPjj5cRwWvyTEEIIIYRQ0OkpVi5ejOLqjyAWu+B2y+D2SOF2yeFwqBASeC7OveBcaLVavm9+/iH4hM/Bp7aU9ENxQQMeHfjTEc+nlqrx+uhXEaIJ6dCFQK/bkwOb14tx/lq8nBhBIYcQQgghhHBU0ekBMnYdQKPnaYSF1x73fqt1G+bMXoqU1GkYOHAQ1q65DyFhDfDYNQjeeQFu7/9/eG74c3zxT51MB61MiyBVEDQyzb9y/FaPF4esdhilEgTKJJAJAupcblyzO4c3HkhRK/B/qdGQCiyZEUIIIYQQQkGn26urqseejNvhb6qF26lEUP658Ept8Eqs8EitsBgOQKVqQGKv1TCbd2Hed7GIjj7AH8taRz8ZPQcPDn8YF8df3CHH7/X5cPWubGwyW1puY4FHLAIqnG6EyKWYkxYLrUTcIcdHCCGEEEI6J6rodGMelxdLf7kRgZGF8HrFCN1xDzTlfhAggUgkBQQJoBBQE/krqqOXQq2pg1qznT9WWjQUvzgOIm5wX0yO67iOanNLa3jIYcFGgAgunw/VLje/TyMW8E1aLEIVHTdHiBBCCCGEdE4UdLop1mjgp28eQWBkU3BR7rsKGdXZeLbf4pZ92DC082wDcElmH8QVjkN19DLURq2AYDPAtX8Yfu//E+YPm99h814qnS48l13CLz8VF4qbw02odXlQ7nShzOFCnEqOKKW8Q46NEEIIIYR0bhR0uqlVi2ZBHz6fX7bnjIEyNwBvD5qHsSFjkWvORWFDIeqd9fhO/Ae+S/kDEe4Q3Fd0LuKz30Wjz4JbEl/CW6PehV6u77Dv4dnsEtS5PUjVKHBjmAmCSASjTMK3FI2yw46LEEIIIYR0fhR0uqEda36FU/4yJIIP9eUJSNg3Ebf1fQdvj38Pg4IH8X0cHgfyzHnYVLoJC7IX4FDtIcyM/hIKrxxeeHFl76swNGRoh30P62ob8L+yWrBa0muJEZBQowFCCCGEENIOFHS6mV0bf0O57R5IpU40mAMRv/MmPJD0Pp4Y+9+WkMPIxXIk+SfxbVrKNGTWZmJB1gIsz1uOSF0k7ul/T4d9Dw6vF48cXgB0WqgR/fXHrvNDCCGEEELIyYh8bDJHJ1dfXw+9Xg+z2QydTtfRh9Np7dv2Owqr7mgJOZFbZ+LDgHkYdfYkTE+djq7irbwyvJJbhgCpBOuH9IJeSnmcEEIIIYS0LxsIJ7yHdCkHdv4VchrNgYjaej9+1C1HwojBXSrkHLLY8XZ+Ob/8bEIYhRxCCCGEEPK3UNDpBjL3/IH88r9CTsy2h/CT9ld4h5vw4KAH0VXkWh24Ylc2HF4fRhk0uCTQr6MPiRBCCCGEdFE0JqiL27ZxDqobnoNU6uYhJ3bbw/hJvRzqs+J4yBFEQpcJOZftzEKJw4UElRwfpkR1WFtrQgghhBDS9VHQ6cL+WP4sPJJZEEt8aKgNQ8KO+/Gz+leEXjgAN/a+scsEhaNDzo/p8TDJpB19WIQQQgghpAujoNMF+XweLF14E+TaNbz9srk0BX323oWftIuRcvkEXJJwCToj1k0tx+qAVBBBIQhQCgKqXG5ctSv7iJATKKeQQwghhBBC/hkKOl2My9WApYunQK07xK+bs0chPetazPX7ESOuvQJjI8ais/H4fPiurAav5Zah1OE67j4UcgghhBBCyOlEQacLqavbiw3rpkGtM8PjEcOxfzIGFE3EB6YvcfG1d2B46HB0Jqxz+YrqeryQU4pMi53fphYLEIsAu8cH5+HO5ulaFWb1iaFKDiGEEEIIOW0o6HQBLDDkZs9Cdu4LkKs8cNhVkO2Zhj5lyXgl7H1cd/WjGBY6rMOOL8tqx+u5Zci3OSEXRJDxTUCl04VdDTa+j59EjHuigjAjLAAKsdBS6bF7vVAJQpeZT0QIIYQQQroGCjqdnNvdiC2b7oHNuQqCGKipDkPg3hsRWaPCs1Hv4JZrnsPQkKEdcmxmlxtv5pXj8+JKuE+w7KxCEOHmcBPujAw8Zk0csUgEtVj87xwsIYQQQgjpUSjodGIuVz1WrTwHgqwCPp8I+Tn9kZx7BfSVRXg0+TPcd9XrGBIy5IweQ7XTjXcLyiEXiRCikCFEJkWIQopd9Va8nFuKGpeH73e2UYerQvx54HF6vXCwYWk+YKy/FqEK2Rk9RkIIIYQQQo5GQacTD1db/du1EOQVcDiUOLR/LIZWjEOR+Ve8MC4Xz5z1FgYGDzyjx+D1+XD7/jysqW084T6sicCz8WEYZ9Sd0WMhhBBCCCGkPSjodFK7d74BkXwfvF4BWXsnYGhxX3xv+BRxV03Cj73fgUKiOOPH8EFBBQ85rA30FSH+qHC4eBvoUoeTDzv7T2QgpocG8HbRhBBCCCGEdCYUdDqh6qo/UVn9MUQCkJs9EEmFEZg/YSvuHfcpIrQRp+Vr1Lnc2Gy2wObx4jyTHnKhqUFAs+1mC17JLeWXX0gIw9WhxtPydQkhhBBCCPk3UNDpZFyuWvy5eQakSh8qK6Ngyh6J2utUeGXQ/f94KNyqmgasrm3AhtpG7Gm0sSk0XKpGgfeTo5CsUfLr9W4Pbtufz+fbTAr043NvCCGEEEII6Uoo6HQiPp8XK5deAanGBptNC+v+81DWrwj3DHzxHz0vaw5wb0Yh5pfXHnF7vEqOGpcb+xrtOHfbQTweG4Kbwk14OLMQBXYnwhVSvJYYTq2fCSGEEEJIl0NBpxP5c/MLkGqy+bycgn0TYZdn4fYpb/+joGFxe3Dj3jysqm2ARARMDfbHSIMWw/00CJZL+byb+zIKsbKmHk9lleCb0hq+uCdb1PPjlOhjWkITQgghhBDSFdBZbCdaL6fO/A3EUiA3azBUFSJc9NQTUEqahpP9HVVON67dnYOdDVbeUODz3tE466juaIFyKeakxWBWSTX+m1XMQw7zUHQIBurV//j7IoQQQgghpCNQ0OkkVi97EmKVC1arDn6H+iPo7gGI0P39xgMFNgeu3JWDHJsD/lIx5vSJRf8TBBdWMZoeFoCRBg2e/v/27j04qvIO4/izu8lmE5IsuZEL5kpigyIBwn1UpMJQRSq0iJehyEWtY0FFrQIK0lLLTAsO7YjDaK1TihYHatMpCtOAt6p0rEqtWAQTiIkk5EJCNoSQy+52zsEEU4gaCNnN4fuZOZM973lP8u7uP3nmfc/vLSrXAGeIFqYPOI93AwAAAAQWQScI+HytarXtVKik2rI8nZjh0Kz0a7r9e+pb27SrtkE7auq166hHjV6f+ZzN5rxByo745nLUgyJc2jQ06xzfBQAAABA8CDpB4F+71ys0/IRaWlzSoUG648c/+daV1A6caNabtR7tPOrRu8eOm5XS2g2NDNfGoVnmszgAAADAxYSgE2BGWKmt2iSnW6o6fLn6T8tUqL3rYNLs82l7db1eN0pF1zboSEtrp+uXRrj0vfhofS/BrWFREbKfRyEDAAAAoK8i6ATYgX0Fcrrr5PWGSEVDNXXeLV0GnM0Vtfrt55U63Hw63LjsNo11R2pCbJSmxLuVFRHWi6MHAAAAghNBJ8CKPl4jV4JUWZGj2PFJCrGHfGPASXSG6AeJMbomNlqj3f0U7rAHaPQAAABAcCLoBFB5xXtyJRyR329T28ERum7F3E7XDzSe1O0fH9ShppaOgLMoPVGzk+PkItwAAAAAXSLoBNAHrz2qiGSpujpdMRkJnWZz3q5r0Py9h+Rp85nlnu8l4AAAAADfGkEnQDyeQwpPPGS+bjyYr1lL7um49qeKo/rp/jKzgpqxNO35IZmKc/JVAQAAAN8W/z0HQFtbg9557WY5o/2qq0tS/7B4OewO+fx+/erQEa37vNLsN31Af63LTWOZGgAAANBNPMXey7zeZhVumypn9FFz35wjn16lG+9bbF77WVF5R8i5Pz1RT1+WTsgBAAAAzgEzOr3I52vT3wumyRlzWG1tofrvx9dqSGqGQkNCte94k579otrs92Ruqm5LjuvNoQEAAACWwoxOL24MWrhlhpwxxfL57Pr0k4nK7H+ppsy527z+i+IK+SRNTXATcgAAAIDzRNDpBU0NZdr5wg0KSfivWUp6/76rlOAcrB/cubCjwtquWo9CbNKjWSm9MSQAAADA0li6doEcP3ZA/y5cowbvP+Uc0Cj7l/ml6MAYRbcN122LF5nnRgGCnxeVm6/npMQrKyLsQg0JAAAAuGgQdC6A7c9dK2dmiRQnOb9sq69PUMXhXEUeH6UfPXK6lHRB1TH953iTIh12PZCRdCGGAwAAAFx0CDo97LVNd5ohx++Xjh1L0tGaNJ2oGSR3U7JiY52a9fB82Ww2s+9Jr0+/PHhqNmdRWqLi2SsHAAAA6BEEnR702Ufb5E1803zwqfxQvlpLJsqV6NKwHw7WiEvzFWoP7dT/+cM1+uJkq5LDQnVnakJPDgUAAAC4qBF0ekhzU60OfL5crkivjtWmyFl/nX60am7H7M3/2994smPPnIczkxThoC4EAAAA0FMIOj1UOvpvW2Yo5hKPmpvDZf/oJt2yat4Z/apbWlVQeUxbK2v1UUOT2Ta4n0uzkmJ7YhgAAAAAvkTQ6QF/eO4mpWZ9YZaO9v57pr6/4lTZ6HZNXp8e3F+mv1bVyev/8oO3SRNjo7UyO0WOLmZ9AAAAAJwbgs55aGpu0u83zVdO+n/M85PFV2vCrYvkCD29DK3V59ddn5So8KjHPB8WFaGZSTGaPiCG4gMAAADABULQOUcFf1mt1pZXlZt5qmrayZpM5UbOVXRGXEcfY4+cxZ+WmiHHZbdp09AsXRkT1TPfHAAAAIAundMT8OvXr1dGRoZcLpfGjBmj995772v7b9myRbm5uWb/K664Qq+++qr6qgOFL+uPT39fEZHPKzahXD6fXf5DVytl31xl33Z1p+d2ln92WFsr68xlas9enkHIAQAAAII16Lz00kt64IEH9Pjjj+vDDz9UXl6epkyZoqqqqrP2f/fdd3XrrbdqwYIF2rNnj6ZPn24ee/fuVV+zde2DOtC8Sim5n8jh8KqlLk2R/7hXY8f8TMMfn92p79qSSj13uMZ8/ZvcNE2Odwdo1AAAAMDFx+Y3ph66wZjBGTVqlJ566inz3OfzKTU1VYsWLdKSJUvO6H/zzTersbFR27Zt62gbO3ashg0bpg0bNpz1bzQ3N5tHO4/HY/6N+vp6RUdHK1D2vLVTVU2LJL9D9k+v05gblig6+/RStXYbSqu0svjUkrYncgZqwSXskQMAAAD0BCMbuN3ub8wG3ZrRaWlp0QcffKBJkyad/gV2u3m+e/fus95jtH+1v8GYAeqqv2H16tXm4NsPI+QEg+FXT1Jb8Rxlx72gyff/+oyQYzyTs6q4vCPkPJSRRMgBAAAAAqBbQaempkZer1eJiYmd2o3zI0eOnPUeo707/Q1Lly41E1r7UVZWpmBgTH49NfqHetYdp5Km0zNOhhafT/fuK9X60lNL+JZmJuvBjM7vGwAAAMBFXHUtLCzMPILN7mON2tNwwjw2Hq7R9Qlu3ZM2QJdGuLRgb4nerGuQwyat/U6qbkk+c0kbAAAAgCAMOvHx8XI4HKqsrOzUbpwnJSWd9R6jvTv9g9m4/v20ddggPV1apddqG7Stut48YkIcqmvzKsJh1+8uz9B34wL3HBEAAACAbi5dczqdys/P165duzrajGIExvm4cePOeo/R/tX+hsLCwi77BzObzWaWiH4xb5BeH/UdzUqKUajNZoacuNAQ/XlYNiEHAAAA6ItL14zS0rfffrtGjhyp0aNHa926dWZVtXnz5pnX58yZo4EDB5oFBQz33XefJkyYoLVr12rq1KnavHmz3n//fT3zzDPqywZHhuu3g9O1NCtZO2o8mhQXrVSXM9DDAgAAAHAuQccoF11dXa0VK1aYBQWMMtE7duzoKDhQWlpqVmJrN378eL344ot67LHHtGzZMuXk5KigoEBDhgyxxBeQHObUvIHxgR4GAAAAgPPZRyeYa2UDAAAAsLYLso8OAAAAAPQFBB0AAAAAlkPQAQAAAGA5BB0AAAAAlkPQAQAAAGA5BB0AAAAAlkPQAQAAAGA5BB0AAAAAlkPQAQAAAGA5BB0AAAAAlkPQAQAAAGA5BB0AAAAAlkPQAQAAAGA5BB0AAAAAlkPQAQAAAGA5BB0AAAAAlhOiPsDv95s/PR5PoIcCAAAAIIDaM0F7RujTQaehocH8mZqaGuihAAAAAAiSjOB2u7u8bvN/UxQKAj6fT+Xl5YqKipLNZgv0cAAAAAAEiBFfjJCTkpIiu93et4MOAAAAAHQHxQgAAAAAWA5BBwAAAIDlEHQAAAAAWA5BBwAAAIDlEHQAAAAAWA5BBwAAAIDlEHQAAH2OsTNCW1tboIcBAAhiBB0AQNBsDr169WplZmYqPDxceXl52rp1q3ntjTfeMDeM3r59u/Lz8xUWFqa3335bxcXFuvHGG5WYmKjIyEiNGjVKO3fuDPRbAQAEgZBADwAAAIMRcjZt2qQNGzYoJydHb731lmbPnq2EhISOD2jJkiVas2aNsrKyFBMTo7KyMl1//fV64oknzPCzceNGTZs2Tfv371daWhofLABcxGx+Y/4fAIAAam5uVmxsrDkbM27cuI72O+64QydOnNBdd92liRMnqqCgwJzB+TpDhgzR3XffrYULF/bCyAEAwYoZHQBAwBUVFZmBZvLkyZ3aW1paNHz48I7zkSNHdrp+/PhxrVy5Uq+88ooqKirM53aamppUWlraa2MHAAQngg4AIOCMwGIwAsvAgQM7XTOWpBnP4hj69evX6dpDDz2kwsJCczlbdna2+WzPzJkzzYAEALi4EXQAAAF32WWXmYHGmImZMGHCGdfbg87/e+eddzR37lzNmDGjIzCVlJRc8PECAIIfQQcAEHBRUVHm7MzixYvN6mtXXnml6uvrzSATHR2t9PT0s95nFC14+eWXzQIERlW25cuXm/cDAEDQAQAEhVWrVpkV1ozqawcPHlT//v01YsQILVu2rMvw8uSTT2r+/PkaP3684uPj9cgjj8jj8fT62AEAwYeqawAAAAAshw1DAQAAAFgOQQcAAACA5RB0AAAAAFgOQQcAAACA5RB0AAAAAFgOQQcAAACA5RB0AAAAAFgOQQcAAACA5RB0AAAAAFgOQQcAAACA5RB0AAAAAMhq/geyA+Wjtil3oAAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 46
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yDsU5ifkpykA"
   },
   "source": [
    "We see that neutralizing against some groups help with CORR while others seem to hurt. Can you think of why this might be the case?\n",
    "\n",
    "Let's see if this same characteristic applies to MMC:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 631
    },
    "id": "76IGP1UGnzNW",
    "outputId": "9e282da6-94fa-410e-c832-19d322aba7df",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:35.941495Z",
     "start_time": "2025-10-30T21:26:34.053463Z"
    }
   },
   "source": [
    "from numerai_tools.scoring import correlation_contribution\n",
    "\n",
    "# Download and join in the meta_model for the validation eras\n",
    "napi.download_dataset(f\"v4.3/meta_model.parquet\", round_num=842)\n",
    "validation[\"meta_model\"] = pd.read_parquet(\n",
    "    f\"v4.3/meta_model.parquet\"\n",
    ")[\"numerai_meta_model\"]\n",
    "\n",
    "# Compute the per-era mmc between our predictions, the meta model, and the target values\n",
    "per_era_mmc = validation.dropna().groupby(\"era\").apply(\n",
    "    lambda x: correlation_contribution(\n",
    "        x[group_neutral_cols], x[\"meta_model\"], x[\"target\"]\n",
    "    )\n",
    ")\n",
    "\n",
    "cumsum_mmc = per_era_mmc.cumsum()\n",
    "\n",
    "cumsum_mmc.plot(\n",
    "  title=\"Cumulative MMC of Neutralized Predictions\",\n",
    "  figsize=(10, 6),\n",
    "  xticks=[]\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:26:34,536 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:26:34,536 INFO numerapi.utils: download complete\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80580/4134733066.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Cumulative MMC of Neutralized Predictions'}, xlabel='era'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 47
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4eFNUepsyu-b"
   },
   "source": [
    "Perhaps the most interesting observation is that neutralizing against `all` of the groups within `small` performs by far the worst in terms of `mean` and `sharpe` of CORR or MMC. Can you think of why this might be the case?"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "YPJcnqWyyu-b",
    "outputId": "67f1eca5-807e-40bf-c0c4-0b8e35a8708d",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:35.953524Z",
     "start_time": "2025-10-30T21:26:35.942194Z"
    }
   },
   "source": [
    "group_neutral_summary_metrics = {}\n",
    "for col in group_neutral_cols:\n",
    "    corr_mean = group_neutral_corr[col].mean()\n",
    "    corr_std = group_neutral_corr[col].std()\n",
    "    corr_sharpe = corr_mean / corr_std\n",
    "    corr_rolling_max = group_neutral_cumsum[col].expanding(min_periods=1).max()\n",
    "    corr_max_drawdown = (corr_rolling_max - group_neutral_cumsum[col]).max()\n",
    "    mmc_mean = per_era_mmc[col].mean()\n",
    "    mmc_std = per_era_mmc[col].std()\n",
    "    mmc_sharpe = mmc_mean / mmc_std\n",
    "    mmc_rolling_max = cumsum_mmc[col].expanding(min_periods=1).max()\n",
    "    mmc_max_drawdown = (rolling_max - cumsum_mmc[col]).max()\n",
    "    group_neutral_summary_metrics[col] = {\n",
    "        \"corr_mean\": corr_mean,\n",
    "        \"mmc_mean\": mmc_mean,\n",
    "        \"corr_std\": corr_std,\n",
    "        \"mmc_std\": mmc_std,\n",
    "        \"corr_sharpe\": corr_sharpe,\n",
    "        \"mmc_sharpe\": mmc_sharpe,\n",
    "        \"corr_max_drawdown\": corr_max_drawdown,\n",
    "        \"mmc_max_drawdown\": mmc_max_drawdown,\n",
    "    }\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "pd.DataFrame(group_neutral_summary_metrics).T"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                          corr_mean  mmc_mean  corr_std  mmc_std  corr_sharpe  \\\n",
       "prediction                 0.016520  0.006914  0.018652 0.016378     0.885694   \n",
       "neutralized_intelligence   0.016724  0.006981  0.018607 0.016106     0.898764   \n",
       "neutralized_wisdom         0.016637  0.006811  0.018322 0.015608     0.908045   \n",
       "neutralized_charisma       0.016331  0.006577  0.018463 0.016334     0.884514   \n",
       "neutralized_dexterity      0.016164  0.007240  0.018592 0.015867     0.869396   \n",
       "neutralized_strength       0.016601  0.007191  0.018638 0.016378     0.890717   \n",
       "neutralized_constitution   0.016341  0.006826  0.018467 0.016325     0.884852   \n",
       "neutralized_agility        0.016710  0.006010  0.018565 0.016761     0.900052   \n",
       "neutralized_serenity       0.016526  0.006763  0.018621 0.016328     0.887534   \n",
       "neutralized_all            0.010678  0.000367  0.017022 0.015585     0.627329   \n",
       "\n",
       "                          mmc_sharpe  corr_max_drawdown  mmc_max_drawdown  \n",
       "prediction                  0.422153           0.040911          1.949824  \n",
       "neutralized_intelligence    0.433412           0.038507          1.944338  \n",
       "neutralized_wisdom          0.436368           0.045409          1.955404  \n",
       "neutralized_charisma        0.402639           0.034907          1.967068  \n",
       "neutralized_dexterity       0.456288           0.043867          1.927256  \n",
       "neutralized_strength        0.439084           0.040946          1.932065  \n",
       "neutralized_constitution    0.418166           0.040313          1.958392  \n",
       "neutralized_agility         0.358560           0.037780          2.007267  \n",
       "neutralized_serenity        0.414211           0.041532          1.957557  \n",
       "neutralized_all             0.023564           0.076985          2.339628  "
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>corr_mean</th>\n",
       "      <th>mmc_mean</th>\n",
       "      <th>corr_std</th>\n",
       "      <th>mmc_std</th>\n",
       "      <th>corr_sharpe</th>\n",
       "      <th>mmc_sharpe</th>\n",
       "      <th>corr_max_drawdown</th>\n",
       "      <th>mmc_max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction</th>\n",
       "      <td>0.016520</td>\n",
       "      <td>0.006914</td>\n",
       "      <td>0.018652</td>\n",
       "      <td>0.016378</td>\n",
       "      <td>0.885694</td>\n",
       "      <td>0.422153</td>\n",
       "      <td>0.040911</td>\n",
       "      <td>1.949824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_intelligence</th>\n",
       "      <td>0.016724</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.018607</td>\n",
       "      <td>0.016106</td>\n",
       "      <td>0.898764</td>\n",
       "      <td>0.433412</td>\n",
       "      <td>0.038507</td>\n",
       "      <td>1.944338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_wisdom</th>\n",
       "      <td>0.016637</td>\n",
       "      <td>0.006811</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>0.015608</td>\n",
       "      <td>0.908045</td>\n",
       "      <td>0.436368</td>\n",
       "      <td>0.045409</td>\n",
       "      <td>1.955404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_charisma</th>\n",
       "      <td>0.016331</td>\n",
       "      <td>0.006577</td>\n",
       "      <td>0.018463</td>\n",
       "      <td>0.016334</td>\n",
       "      <td>0.884514</td>\n",
       "      <td>0.402639</td>\n",
       "      <td>0.034907</td>\n",
       "      <td>1.967068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_dexterity</th>\n",
       "      <td>0.016164</td>\n",
       "      <td>0.007240</td>\n",
       "      <td>0.018592</td>\n",
       "      <td>0.015867</td>\n",
       "      <td>0.869396</td>\n",
       "      <td>0.456288</td>\n",
       "      <td>0.043867</td>\n",
       "      <td>1.927256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_strength</th>\n",
       "      <td>0.016601</td>\n",
       "      <td>0.007191</td>\n",
       "      <td>0.018638</td>\n",
       "      <td>0.016378</td>\n",
       "      <td>0.890717</td>\n",
       "      <td>0.439084</td>\n",
       "      <td>0.040946</td>\n",
       "      <td>1.932065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_constitution</th>\n",
       "      <td>0.016341</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>0.018467</td>\n",
       "      <td>0.016325</td>\n",
       "      <td>0.884852</td>\n",
       "      <td>0.418166</td>\n",
       "      <td>0.040313</td>\n",
       "      <td>1.958392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_agility</th>\n",
       "      <td>0.016710</td>\n",
       "      <td>0.006010</td>\n",
       "      <td>0.018565</td>\n",
       "      <td>0.016761</td>\n",
       "      <td>0.900052</td>\n",
       "      <td>0.358560</td>\n",
       "      <td>0.037780</td>\n",
       "      <td>2.007267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_serenity</th>\n",
       "      <td>0.016526</td>\n",
       "      <td>0.006763</td>\n",
       "      <td>0.018621</td>\n",
       "      <td>0.016328</td>\n",
       "      <td>0.887534</td>\n",
       "      <td>0.414211</td>\n",
       "      <td>0.041532</td>\n",
       "      <td>1.957557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutralized_all</th>\n",
       "      <td>0.010678</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.017022</td>\n",
       "      <td>0.015585</td>\n",
       "      <td>0.627329</td>\n",
       "      <td>0.023564</td>\n",
       "      <td>0.076985</td>\n",
       "      <td>2.339628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 48
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GjDZ4rP6yu-c"
   },
   "source": [
    "**Based** on our simple analysis above, it seems like neutralizing `serenity` group within `small` at porportion of 1 may be the best choice for performance. What do you think?\n",
    "\n",
    "In your research, you may want to experiment with neutralizing different subsets of features at different porportions and make your own judgement on how to balance the risk reward benefits of neutralization. You may even consider incorporating neutralization into the objective function of your training instead of applying it to predictions like we do here.\n",
    "\n",
    "Lastly, whether you want to apply feature neutralization to your model or not is completely up to you. In fact, many great performing models have no feature neutralization at all!   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_mqza1fTyu-c"
   },
   "source": [
    "## 4. Building a feature-neutral model\n",
    "\n",
    "To wrap up this notebook, let's build and upload our new feature neutral model."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "ds3HPim6M-s5",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:35.957376Z",
     "start_time": "2025-10-30T21:26:35.954354Z"
    }
   },
   "source": [
    "# We copy this neutralization code here because Numerai's model upload framework\n",
    "# does not currently include numerai-tools\n",
    "\n",
    "def neutralize(\n",
    "    df: pd.DataFrame,\n",
    "    neutralizers: np.ndarray,\n",
    "    proportion: float = 1.0,\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"Neutralize each column of a given DataFrame by each feature in a given\n",
    "    neutralizers DataFrame. Neutralization uses least-squares regression to\n",
    "    find the orthogonal projection of each column onto the neutralizers, then\n",
    "    subtracts the result from the original predictions.\n",
    "\n",
    "    Arguments:\n",
    "        df: pd.DataFrame - the data with columns to neutralize\n",
    "        neutralizers: pd.DataFrame - the neutralizer data with features as columns\n",
    "        proportion: float - the degree to which neutralization occurs\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame - the neutralized data\n",
    "    \"\"\"\n",
    "    assert not neutralizers.isna().any().any(), \"Neutralizers contain NaNs\"\n",
    "    assert len(df.index) == len(neutralizers.index), \"Indices don't match\"\n",
    "    assert (df.index == neutralizers.index).all(), \"Indices don't match\"\n",
    "    df[df.columns[df.std() == 0]] = np.nan\n",
    "    df_arr = df.values\n",
    "    neutralizer_arr = neutralizers.values\n",
    "    neutralizer_arr = np.hstack(\n",
    "        # add a column of 1s to the neutralizer array in case neutralizer_arr is a single column\n",
    "        (neutralizer_arr, np.array([1] * len(neutralizer_arr)).reshape(-1, 1))\n",
    "    )\n",
    "    inverse_neutralizers = np.linalg.pinv(neutralizer_arr, rcond=1e-6)\n",
    "    adjustments = proportion * neutralizer_arr.dot(inverse_neutralizers.dot(df_arr))\n",
    "    neutral = df_arr - adjustments\n",
    "    return pd.DataFrame(neutral, index=df.index, columns=df.columns)"
   ],
   "outputs": [],
   "execution_count": 49
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "PMGoxkbuyu-c",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:35.960291Z",
     "start_time": "2025-10-30T21:26:35.958354Z"
    }
   },
   "source": [
    "def predict_neutral(live_features: pd.DataFrame) -> pd.DataFrame:\n",
    "    # make predictions using all features\n",
    "    predictions = pd.DataFrame(\n",
    "        model.predict(live_features[small_features]),\n",
    "        index=live_features.index,\n",
    "        columns=[\"prediction\"]\n",
    "    )\n",
    "    # neutralize predictions to a subset of features\n",
    "    neutralized = neutralize(predictions, live_features[med_serenity_feats])\n",
    "    return neutralized.rank(pct=True)"
   ],
   "outputs": [],
   "execution_count": 50
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "id": "JfJd0vL4yu-c",
    "outputId": "7f47c616-27ad-43b6-98fe-19a54eadd3b2",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:37.105134Z",
     "start_time": "2025-10-30T21:26:35.961182Z"
    }
   },
   "source": [
    "# Quick test\n",
    "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
    "live_features = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\", columns=small_features)\n",
    "predict_neutral(live_features)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:26:36,423 INFO numerapi.utils: starting download\n",
      "\n",
      "v5.1/live.parquet:   0%|          | 0.00/9.03M [00:00<?, ?B/s]\u001B[A\n",
      "v5.1/live.parquet:   1%|▏         | 124k/9.03M [00:00<00:07, 1.15MB/s]\u001B[A\n",
      "v5.1/live.parquet:  12%|█▏        | 1.05M/9.03M [00:00<00:01, 5.76MB/s]\u001B[A\n",
      "v5.1/live.parquet:  34%|███▍      | 3.11M/9.03M [00:00<00:00, 12.2MB/s]\u001B[A\n",
      "v5.1/live.parquet: 9.03MB [00:00, 18.8MB/s]                            \u001B[A\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                  prediction\n",
       "id                          \n",
       "n000216cc4494713    0.727896\n",
       "n000a84b4f40eca0    0.895244\n",
       "n0011889f1d1f315    0.021156\n",
       "n0020dbf0bd45ef4    0.017216\n",
       "n002f65628075a89    0.256784\n",
       "...                      ...\n",
       "nffcea5e1ee074fc    0.654362\n",
       "nffeb4bd146a7d3f    0.338780\n",
       "nfffa1a1e897fb6f    0.533995\n",
       "nfffecb08bf96924    0.861687\n",
       "nffffb5d0acb74c0    0.984535\n",
       "\n",
       "[6854 rows x 1 columns]"
      ],
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       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
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       "      <th>n0011889f1d1f315</th>\n",
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       "      <th>n002f65628075a89</th>\n",
       "      <td>0.256784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>nffcea5e1ee074fc</th>\n",
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       "      <th>nfffa1a1e897fb6f</th>\n",
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       "<p>6854 rows × 1 columns</p>\n",
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      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "g5A5r1ZFyu-c",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:37.152097Z",
     "start_time": "2025-10-30T21:26:37.107777Z"
    }
   },
   "source": [
    "# Use the cloudpickle library to serialize your function and its dependencies\n",
    "import cloudpickle\n",
    "p = cloudpickle.dumps(predict_neutral)\n",
    "with open(\"feature_neutralization.pkl\", \"wb\") as f:\n",
    "    f.write(p)"
   ],
   "outputs": [],
   "execution_count": 52
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "J813OXJ6yu-d",
    "outputId": "dd687ffc-2dc4-41f3-cf61-764862809432",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:37.159080Z",
     "start_time": "2025-10-30T21:26:37.154491Z"
    }
   },
   "source": [
    "# Download file if running in Google Colab\n",
    "try:\n",
    "    from google.colab import files\n",
    "    files.download('feature_neutralization.pkl')\n",
    "except:\n",
    "    pass"
   ],
   "outputs": [],
   "execution_count": 53
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wqBNYzHpyu-d"
   },
   "source": [
    "That's it! Now head back to [numer.ai](numer.ai) to upload your model!"
   ]
  }
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